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U.S. GLOBAL CHANGE RESEARCH PROGRAM 
CLIMATE SCIENCE SPECIAL REPORT (CSSR) 


Public Comment Period 

15 December 2016 - 3 February 2017 
[revised end date] 


Third-Order Draft (TOD) 


COORDINATING LEAD AUTHORS 

Donald Wuebbles David Fahey Kathleen Hibbard 

Office of Science & Technology Policy NOAA Earth System Research Lab NASA Headquarters 

Executive Office of the President 


LEAD AUTHORS 


Jeff Arnold, U.S. Army Corps of Engineers 
Benjamin DeAngelo, U.S. Global Change Research Program 
Sarah Doherty, University of Washington 
David Easterling, NOAA National Centers for Environmental 
Information 

James Edmonds, Pacific Northwest National Laboratory 
Timothy Hall, NASA Goddard Institute for Space Studies 
Katharine Hay hoe, Texas Tech University 
Forrest Hoffman, Oak Ridge National Laboratory 
Radley Horton, Columbia University 
Deborah Huntzinger, Northern Arizona University 
Libby Jewett, NOAA Ocean Acidification Program 
Thomas Knutson, NOAA Geophysical Fluid Dynamics Lab 
Robert Kopp, Rutgers University 

James Kossin, NOAA National Centers for Environmental 
Information 

Kenneth Kunkel, North Carolina State University 


Allegra LeGrande, NASA Goddard Institute for Space Studies 
L. Ruby Leung, Pacific Northwest National Laboratory 
Wieslaw Maslowski, Naval Postgraduate School 
Carl Mears, Remote Sensing Systems 
Judith Perlwitz, NOAA Earth System Research Laboratory 
Anastasia Romanou, Columbia University 
Benjamin Sanderson, National Center for Atmospheric Research 
William Sweet, NOAA National Ocean Service 
Patrick Taylor, NASA Langley Research Center 
Robert Trapp, University of Illinois at Urbana-Champaign 
Russell Vose, NOAA National Centers for Environmental 
Information 

Duane Waliser, NASA Jet Propulsion Laboratory 
Chris Weaver, USEPA 

Michael Wehner, Lawrence Berkeley National Laboratory 
Tristram West, DOE Office of Science 


CONTRIBUTING AUTHORS 


Richard Alley, Penn State University 
Shallin Busch, NOAA Ocean Acidification Program 
Sarah Champion, North Carolina State University 
Imke Durre, NOAA National Centers for Environmental 
Information 

Dwight Gledhill, NOAA Ocean Acidification Program 
Justin Goldstein, U.S. Global Change Research Program 


Lisa Levin, University of California - San Diego 
Allan Rhoades, University of California - Davis 
Paul Ullrich, University of California - Davis 
Eugene Wahl, NOAA National Centers for Environmental 
Information 

John Walsh, University of Alaska Fairbanks 


December 2016 


CSSR Public Comment Period 


U.S. GLOBAL CHANGE RESEARCH PROGRAM 
CLIMATE SCIENCE SPECIAL REPORT (CSSR) 

Third- Order Draft 
Table of Contents 

Front Matter 

About This Report 1 

Guide to the Report 3 

Executive Summary 11 

Chapters 

1. Our Globally Changing Climate 32 

2. Physical Drivers of Climate Change 85 

3. Detection and Attribution of Climate Change 139 

4. Climate Models, Scenarios, and Projections 152 

5. Large-Scale Circulation and Climate Variability 186 

6. Temperature Changes in the United States 217 

7. Precipitation Change in the United States 252 

8. Droughts, Floods, and Hydrology 281 

9. Extreme Storms 308 

10. Changes in Land Cover and Terrestrial Biogeochemistry 337 

1 1 . Arctic Changes and their Effects on Alaska and the Rest of the United States 370 

12. Sea Level Rise 411 

13. Ocean Changes: Warming, Stratification, Circulation, Acidification, and Deoxygenation 452 

14. Perspectives on Climate Change Mitigation 481 

15. Potential Surprises: Compound Extremes and Tipping Elements 500 

Appendices 

A. Observational Datasets Used in Climate Studies 523 

B. Weighting Strategy for the 4th National Climate Assessment 529 

C. Acronyms and Units 539 

D. Glossary TBD 




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Front Matter 


About This Report 

As a key input into the Fourth National Climate Assessment (NCA4), the U.S. Global Change 
Research Program (USGCRP) oversaw the production of this special, stand-alone report of the 
state of science relating to climate change and its physical impacts. The Climate Science Special 
Report (CSSR) serves several purposes for NCA4, including providing 1) an updated detailed 
analysis of the findings of how climate change is affecting weather and climate across the United 
States, 2) an executive summary that will be used as the basis for the science summary of NCA4, 
and 3) foundational information and projections for climate change, including extremes, to 
improve “end-to-end” consistency in sectoral, regional, and resilience analyses for NCA4. This 
report allows NCA4 to focus more heavily on the human welfare, societal, and environmental 
elements of climate change, in particular with regard to observed and projected risks, impacts, 
adaptation options, regional analyses, and implications (such as avoided risks) of known 
mitigation actions. 

Much of this report is intended for a scientific and technically savvy audience, though the 
Executive Summary is designed to be accessible to a broader audience. 

Report Development, Review, and Approval Process 

The National Oceanic and Atmospheric Administration (NOAA) served as the administrative 
lead agency for the preparation of this report. The Science Steering Committee (SSC 1 ) comprises 
representatives from three agencies (NOAA, NASA, and DOE) and the U.S. Global Change 
Research Program (USGCRP), 2 and three Coordinating Lead Authors, all of whom were Federal 
employees during the development of this report. Following a public notice for author 
nominations, the SSC selected 30 Lead Authors, who are scientists representing Federal 
agencies, national laboratories, universities, and the private sector. Contributing Authors were 
later chosen to provide special input on select areas of the assessment. 

The Sustained National Climate Assessment 

The Climate Science Special Report has been developed as part of the USGCRP’ s sustained 
National Climate Assessment (NCA) process. This process facilitates continuous and transparent 
participation of scientists and stakeholders across regions and sectors, enabling new information 


1 The Science Steering Committee is a federal advisory committee that oversees the production of the CSSR. 

2 The USGCRP is made up of 13 Federal departments and agencies that carry out research and support the Nation’s 
response to global change. The USGCRP is overseen by the Subcommittee on Global Change Research (SGCR) of 
the National Science and Technology Council’s Committee on Environment, Natural Resources, and Sustainability 
(CENRS), which in turn is overseen by the White House Office of Science and Technology Policy (OSTP). The 
agencies within USGCRP are the Department of Agriculture, the Department of Commerce (NOAA), the 
Department of Defense, the Department of Energy, the Department of Health and Human Services, the Department 
of the Interior, the Department of State, the Department of Transportation, the Environmental Protection Agency, 
the National Aeronautics and Space Administration, the National Science Foundation, the Smithsonian Institution, 
and the U.S. Agency for International Development. 


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and insights to be assessed as they emerge. Relative to other analyses done under the sustained 
assessment process, the Climate Science Special Report provides a more comprehensive 
assessment of the science underlying the changes occurring in the Earth’s climate system, with a 
special focus on the United States. 

Sources Used in this Report 

The findings in this report are based on a large body of scientific, peer-reviewed research, as well 
as a number of other publicly available sources, including well-established and carefully 
evaluated observational and modeling datasets. The team of authors carefully reviewed these 
sources to ensure a reliable assessment of the state of scientific understanding. Each source of 
information was detennined to meet the four parts of the IQ A Guidance provided to authors: 1) 
utility, 2) transparency and traceability, 3) objectivity, and 4) integrity and security. Report 
authors assessed and synthesized in formation from peer-reviewed journal articles, technical re- 
ports produced by federal agencies, scientific assessments (such as IPCC 2013), reports of the 
National Academy of Sciences and its associated National Research Council, and various 
regional climate impact assessments, conference proceedings, and government statistics (such as 
population census and energy usage). 


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Front Matter 


1 Guide to the Report 

2 The following describes the format of the Climate Science Special Report and the overall 

3 structure and features of the chapters. 

4 Executive Summary 

5 The Executive Summary describes the major findings from the Climate Science Special Report. 

6 It summarizes the overall findings and includes some key figures and additional bullet points 

7 covering overarching and especially noteworthy conclusions. The Executive Summary and the 

8 majority of the Key Findings are written for the non-expert. 

9 Chapters 

1 0 Key Findings and Traceable Accounts 

1 1 Each topical chapter includes Key Findings, which are based on the authors’ expert judgment of 

12 the synthesis of the assessed literature. Each Key Finding includes a confidence statement and, as 

13 appropriate, framing of key scientific uncertainties, so as to be better support assessment of 

14 climate -related risks. (See “Documenting Uncertainty” below). 

15 Each Key Finding is also accompanied by a Traceable Account that documents the supporting 

1 6 evidence, process, and rationale the authors used in reaching these conclusions and provides 

17 additional information on sources of uncertainty through confidence and likelihood statements. 

1 8 The Traceable Accounts can be found at the end of each chapter. 

1 9 Regional Analyses 

20 Throughout the report, the regional analyses of climate changes for the United States are based 

21 on ten different regions as shown in Figure 1. There are differences from the regions used in the 

22 Third National Climate Assessment (Melillo et al. 2014): 1) the Great Plains are split into the 

23 Northern Great Plains and Southern Great Plains; and 2) The U.S. islands in the Caribbean are 

24 analyzed as a separate region apart from the Southeast. 

25 Chapter Text 

26 Each chapter assesses the state of the science for a particular aspect of the changing climate. The 

27 first chapter gives a summary of the global changes occurring in the Earth’s climate system. This 

28 is followed in Chapter 2 by a summary of the scientific basis for climate change. Chapter 3 gives 

29 an overview of the processes used in the detection and attribution of climate change and 

30 associated studies using those techniques. Chapter 4 then discusses the scenarios for greenhouse 

31 gases and particles and the modeling tools used to study future projections. Chapters 5 through 9 

32 primarily focus on physical changes in climate occurring in the United States, including those 

33 projected to occur in the future. Chapter 10 provides a focus on land use change and associated 

34 feedbacks on climate. Chapter 1 1 addresses changes in Alaska in the Arctic, and how the latter 


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Front Matter 


1 affects the United States. Chapters 12 and 13 discuss key issues connected with sea level rise and 

2 ocean changes, including ocean acidification, and their potential effects on the United States. 

3 Finally, Chapters 14 and 15 discuss some important perspectives on how mitigation activities 

4 could affect future changes in climate and provide perspectives on what surprises could be in 

5 store for the changing climate beyond the analyses already covered in the rest of the assessment. 

6 This report is designed to be an authoritative assessment of the science of climate change, with a 

7 focus on the United States, to serve as the foundation for efforts to assess climate -related risks 

8 and inform decision-making about responses. In accordance with this purpose, it does not 

9 include an assessment of literature on climate change mitigation, adaptation, economic valuation, 

10 or societal responses, nor does it include policy recommendations. 

1 1 Throughout the report, results are presented in American units as well as in the International 

12 System of Units. 

1 3 Reference time periods for graphics 

14 There are many different types of graphics in the Climate Science Special Report. Some of the 

1 5 graphs in this report illustrate historical changes and future trends in climate compared to some 

1 6 reference period, with the choice of this period determined by the purpose of the graph and the 

1 7 availability of data. Where graphs were generated for this report, they are mostly based on one of 

18 two reference periods. The 1901-1960 reference period is particularly used for graphs that 

19 illustrate past changes in climate conditions, whether in observations or in model simulations. 

20 This 60-year time period was also used for analyses in the Third National Climate Assessment 

21 (NCA3; Melillo et al. 2014). The beginning date was chosen because earlier historical 

22 observations are generally considered to be less reliable. Thus, these graphs are able to highlight 

23 the recent, more rapid changes relative to the early part of the century (the reference period) and 

24 also reveal how well the climate models simulate these observed changes. In this report, this time 

25 period is used as the base period in most maps of observed trends and all time-varying, area- 

26 weighted averages that show both observed and projected quantities. 

27 The other commonly used reference period in this report is 1976-2005. The choice of a 30-year 

28 period is consistent with the World Meteorological Organization’s recommendation for climate 

29 statistics. This period is used for graphs that illustrate projected changes simulated by climate 

30 models. The purpose of these graphs is to show projected changes compared to a period that 

31 allows stakeholders and decision makers to base fundamental planning and decisions on average 

32 and extreme climate conditions in a non-stationary climate; thus, a recent available 30-year 

33 period was chosen (Arguez and Vose 2011). The year 2005 was chosen as an end date because 

34 the historical period simulated by the models used in this assessment ends in that year. 

35 For future projections, 30-year periods are again used for consistency. Projections are centered 

36 around 2030, 2050, and 2085 with an interval of plus and minus 15 years (for example, results 

37 for 2030 cover the period 2015-2045); Most model runs used here only project out to 2100 for 


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future scenarios, but where possible, results beyond 2100 are shown. Note that these time periods 
are different than those used in some of the graphics in NCA3. There are also exceptions for 
graphics that are based on existing publications. 

For global results that may be dependent on findings from other assessments (such as those 
produced by the Intergovernmental Panel on Climate Change, or IPCC), and for other graphics 
that depend on specific published work, the use of other time periods was also allowed, but an 
attempt was made to keep them as similar to the selected periods as possible. For example, in the 
discussion of radiative forcing, the report uses the standard analyses from IPCC for the industrial 
era (1750 to 2011) (following IPCC 2013). And, of course, the paleoclimatic discussion of past 
climates goes back much further in time. 

Model Results: Past Trends and Projected Futures 

While the NCA3 included global modeling results from both the CMIP3 (Coupled Model 
Intercomparison Project, 3rd phase) models used in the 2007 international assessment (IPCC 
2007) and the CMIP5 (Coupled Model Intercomparison Project, 5th phase) models used in the 
more recent international assessment (IPCC 2013), the primary focus in this assessment is the 
global model results and associated downscaled products from CMIP5. The CMIP5 models and 
the associated downscaled products are discussed in Chapter 4. 

Treatment of Uncertainties: Likelihoods, Confidence, and Risk Framing 

Throughout this report’s assessment of the scientific understanding of climate change, the 
authors have assessed to the fullest extent possible the range of information in the scientific 
literature to arrive at a series of findings referred to as Key Findings. The approach used to 
represent the state of certainty in this understanding as represented in the Key Findings is done 
through two metrics: 

• Confidence in the validity of a finding based on the type, amount, quality, strength, and 
consistency of evidence (such as mechanistic understanding, theory, data, models, and expert 
judgment); the skill, range, and consistency of model projections; and the degree of 
agreement within the body of literature. 

• Likelihood, or probability of an effect or impact occurring, is based on measures of 
uncertainty expressed probabilistically (in other words, based on statistical analysis of 
observations or model results or on the authors’ expert judgment). 

The terminology used in the report associated with these metrics is shown in Figure 2. This 
language is based on that used in NCA3 (Melillo et al. 2014), the IPCC’s Fifth Assessment 
Report (IPCC 2013), and most recently the USGCRP Climate and Health assessment (USGCRP 
2016). Wherever used, the confidence and likelihood statements are italicized. 


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Front Matter 


Assessments of confidence in the Key Findings are based on the expert judgment of the author 
team. Authors provide supporting evidence for each of the chapter’s Key Findings in the 
Traceable Accounts. Confidence is expressed qualitatively and ranges from low confidence 
(inconclusive evidence or disagreement among experts) to very high confidence (strong evidence 
and high consensus) (see Figure 2). Confidence should not be interpreted probabilistically, as it 
is distinct from statistical likelihood. 

In this report, likelihood is the chance of occurrence of an effect or impact based on measures of 
uncertainty expressed probabilistically (in other words, based on statistical analysis of 
observations or model results or on expert judgment). The authors used expert judgment based 
on the synthesis of the literature assessed to arrive at an estimation of the likelihood that a 
particular observed effect was related to human contributions to climate change or that a 
particular impact will occur within the range of possible outcomes. Where it is considered 
justified to report the likelihood of particular impacts within the range of possible outcomes, this 
report takes a plain-language approach to expressing the expert judgment of the chapter team, 
based on the best available evidence. For example, an outcome termed “likely” has at least a 
66% chance of occurring; an outcome termed “very likely,” at least a 90% chance. See Figure 2 
for a complete list of the likelihood terminology used in this report. 

Traceable Accounts for each Key Finding 1) document the process and rationale the authors used 
in reaching the conclusions in their Key Finding, 2) provide additional information to readers 
about the quality of the information used, 3) allow traceability to resources and data, and 4) 
describe the level of likelihood and confidence in the Key Finding. Thus, the Traceable Accounts 
represent a synthesis of the chapter author team’s judgment of the validity of findings, as 
determined through evaluation of evidence and agreement in the scientific literature. The 
Traceable Accounts also identify areas where data are limited or emerging. Each Traceable 
Account includes 1) a description of the evidence base, 2) major uncertainties, and 3) an 
assessment of confidence based on evidence. 

All Key Findings include a description of confidence. Where it is considered scientifically 
justified to report the likelihood of particular impacts within the range of possible outcomes, Key 
Findings also include a likelihood designation. 

Confidence and likelihood levels are based on the expert assessment of the author team. They 
determined the appropriate level of confidence or likelihood by assessing the available literature, 
determining the quality and quantity of available evidence, and evaluating the level of agreement 
across different studies. Often, the underlying studies provided their own estimates of uncertainty 
and confidence intervals. When available, these confidence intervals were assessed by the 
authors in making their own expert judgments. For specific descriptions of the process by which 
the author team came to agreement on the Key Findings and the assessment of confidence and 
likelihood, see the Traceable Accounts in each chapter. 


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In addition to the use of systematic language to convey confidence and likelihood information, 
this report attempts to highlight aspects of the science that are most relevant for supporting the 
assessment (for example, in the upcoming fourth National Climate Assessment) of key societal 
risks posed by climate change. This includes attention to the tails of the probability distribution 
of future climate change and its proximate impacts (for example, on sea level or temperature and 
precipitation extremes) and on defining plausible bounds for the magnitude of future changes, 
since many key risks are disproportionately determined by low-probability, high-consequence 
outcomes. Therefore, in addition to presenting the “most likely” or “best guess” range of 
projected future climate outcomes, where appropriate, this report also provides information on 
the outcomes lying outside this range which nevertheless cannot be ruled out, and may therefore 
be relevant for assessing overall risk. In some cases, this involves an evaluation of the full range 
of information contained in the ensemble of climate models used for this report, and in other 
cases will involve the consideration of additional lines of scientific evidence beyond the models. 

Complementing this use of risk-focused language and presentation around specific scientific 
findings in the report, Chapter 1 5 provides an overview of potential surprises resulting from 
climate change, including tipping elements in the climate system and the compounding effects of 
multiple, interacting climate change impacts whose consequences may be much greater than the 
sum of the individual impacts. Chapter 15 also highlights critical knowledge gaps that detennine 
the degree to which such high-risk tails and bounding scenarios can be precisely defined, 
including missing processes and feedbacks that make it more likely than not that climate models 
currently underestimate the potential for high-end changes, reinforcing the need to look beyond 
the central tendencies of model projections to meaningfully assess climate change risk. 


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Northwest 


Northern 
Great Plains 


Northeast 




Midwest 


Southwest 


Southern 
Great Plains 


Southeast 


Hawai'i 

and 

Pacific Islands 


Caribbean 

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2 Figure 1. Map of the ten regions of the United States used throughout the Climate Science 

3 Special Report. Regions are similar to that used in the Third National Climate Assessment except 

4 that 1) the Great Plains are split into the Northern Great Plains and Southern Great Plains, and 2) 

5 the Caribbean islands have been split from the Southeast region. (Figure source: adapted from 

6 Melillo et al. 2014). 

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1 

2 

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4 

5 

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Confidence Level 


Likelihood 


Very High 


Strong evidence (established 
theory, multiple sources, consistent 
results, well documented and 
accepted methods, etc.), high 
consensus 


Moderate evidence (several sourc- 
es, some consistency, methods 
vary and/or documentation limited, 
etc.), medium consensus 


Medium 


Suggestive evidence (a few sourc- 
es, limited consistency, models 
incomplete, methods emerging, 
etc.), competing schools of thought 


Inconclusive evidence (limited 
sources, extrapolations, inconsis- 
tent findings, poor documentation 
and/or methods not tested, etc.), 
disagreement or lack of opinions 
among experts 


Virtually Certain 


99%-100% 


Extremely Likely 


95%-100% 


Very Likely 


90%-100% 


66 %- 100 % 


About as Likely as Not 


33%-66% 


Unlikely 


0%-33% 


Very Unlikely 


0 %- 10 % 


Extremely Unlikely 


0%-5% 


Exceptionally Unlikely 


0%-P/o 


Figure 2. Confidence levels and likelihood statements used in the report. (Figure source: adapted 
from USGCRP 2016 and IPCC 2013). 


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1 Front Matter References 

2 Arguez, A. and R.S. Vose, 2011: The definition of the standard WMO climate normal: The key 

3 to deriving alternative climate normals. Bulletin of the American Meteorological Society, 92, 

4 699-704. http://dx.doi.org/10.1175/2010BAMS2955T 

5 IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group 

6 I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. 

7 Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. 

8 Miller, Eds. Cambridge University Press, Cambridge. U.K, New York, NY, USA, 996 pp. 

9 www .ipcc .ch/publications_and_data/publications_ipcc_fourth_assessment_report_wg l_repor 

1 0 t_the_physical_science_basis.htm 

1 1 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

12 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

13 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

14 http://dx.doi.org/10.1017/CB09781 107415324 www .climatechange20 1 3 .org 

15 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

1 6 States: The Third National Climate Assessment. 2014, U.S . Global Change Research 

17 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 

18 OMB, 2002: Guidelines for Ensuring and Maximizing the Quality, Objectivity, Utility, and 

1 9 Integrity of Information Disseminated by Federal Agencies; Republication. Federal Register, 

20 67, 8452-8460. https://www.whitehouse.gov/sites/default/files/omb/fedreg/reproducible2.pdf 

21 USGCRP, 2016: The Impacts of Climate Change on Human Health in the United States: A 

22 Scientific Assessment. Crimmins, A., J. Balbus, J.L. Gamble, C.B . Beard, J.E. Bell, D. 

23 Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. 

24 Saha, M.C. Sarofim, J. Trtanj, and L. Ziska, Eds. U.S. Global Change Research Program, 

25 Washington, DC, 312 pp. http://dx.doi.org/10.7930/J0R49NQX 

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Executive Summary 


1 U.S. GLOBAL CHANGE RESEARCH PROGRAM 

2 CLIMATE SCIENCE SPECIAL REPORT (CSSR) 

3 Executive Summary 

4 Introduction 

5 New observations and new research have increased scientists’ understanding of past, current, 

6 and future climate change since the Third U.S. National Climate Assessment (NCA3) was 

7 published in May 2014. This Climate Science Special Report (CSSR) is designed to capture 

8 that new information, build on the existing body of science, and summarize the current state 

9 of knowledge. 

10 Predicting how climate will change in future decades is a different scientific issue from 

1 1 predicting weather a few weeks from now. Weather is what is happening in the atmosphere in 

12 a given location at a particular time — temperature, humidity, winds, clouds, and precipitation. 

13 Climate consists of the patterns exhibited by the weather — the averages and extremes of the 

14 indicated weather phenomena and how those averages and extremes vary from month to 

15 month over the course of a typical year — as observed over a period of decades. One can 

16 sensibly speak of the climate of a specific location (for example, Chicago) or a region (for 

17 example, the Midwest). Climate change means that these weather patterns — the averages and 

18 extremes and their timing — are shifting in consistent directions from decade to decade. 

19 The world has warmed (globally and annually averaged surface air temperature) by about 

20 1.6°F (0.9°C) over the last 150 years (1865-2015), and the spatial and temporal non- 

21 uniformity of the warming has triggered many other changes to the Earth’s climate. Evidence 

22 for a changing climate abounds, from the top of the atmosphere to the depths of the oceans. 

23 Thousands of studies conducted by tens of thousands of scientists around the world have 

24 documented changes in surface, atmospheric, and oceanic temperatures; melting glaciers; 

25 disappearing snow cover; shrinking sea ice; rising sea level; and an increase in atmospheric 

26 water vapor. Many lines of evidence demonstrate that human activities, especially emissions 

27 of greenhouse (heat-trapping) gases, are primarily responsible for recent observed climate 

28 changes. 

29 The last few years have also seen record-breaking, climate-related, weather extremes, as well 

30 as the warmest years on record for the globe. Periodically taking stock of the current state of 

3 1 knowledge about climate change and putting new weather extremes into context ensures that 

32 rigorous, scientifically based infonnation is available to inform dialogue and decisions at 

33 every level. 

34 Most of this special report is intended for those who have a technical background in climate 

35 science and is also designed to provide input to the authors of the Fourth U.S. National 

36 Climate Assessment (NCA4). In this executive summary, green boxes present highlights of 

37 the main report followed by related bullet points and selected figures covering more scientific 


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Executive Summary 


1 details. The summary material on each topic presents the most salient points of chapter 

2 findings and therefore represents only a subset of the report contents. For more details, the 

3 reader is referred to the content of individual chapters. This report discusses climate trends 

4 and findings at several scales: global, nationwide for the United States, and according to ten 

5 specific U.S. regions (shown in Figure 1 in the Guide to the Report). A statement of scientific 

6 confidence also follows each bullet in the executive summary. The confidence scale is 

7 described in the Guide to the Report. 

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Global and U.S. Temperatures Will Continue to Rise 

Long-term temperature observations are among the most consistent and widespread evidence 
of a warming planet. Temperature (and, above all, its local averages and extremes) affects 
agricultural productivity, energy use, human health, infrastructure, natural ecosystems, and 
many other essential aspects of society and the natural environment. (Ch. 1) 

Observed Global and U.S. Temperature 


The global, long-term, and unambiguous warming trend has continued during 
recent years. Since the last National Climate Assessment was published, 2014 
became the warmest year on record up to that time; 2015 surpassed 2014 by a 
wide margin; and 2016 is expected to surpass 2015. Fifteen of the last 16 years 
are the warmest years on record for the globe. (Ch.l; Fig ES. 1) 


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• Global annual average temperature, measured over both land and ocean, has increased by 
more than 1.6°F (0.9°C) from 1880 through 2015 ( very high confidence). Longer-term 
climate records indicate that average temperatures in recent decades over much of the 
world have been much higher than at any time in at least the past 1700 years ( high 
confidence). (Ch.l) 

• Many lines of evidence demonstrate that human activities, especially emissions of 
greenhouse gases, are primarily responsible for observed climate changes in the industrial 
era. There are no alternative explanations, and no natural cycles are found in the 
observational record that can explain the observed changes in climate. ( Very high 
confidence) (Ch.l) 

• The likely range of the human contribution to the global mean temperature increase over 
the period 1951-2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed 
warming of 1.2°F (0.65°C) over this period (high confidence). It is extremely likely that 
most of the global mean temperature increase since 1951 was caused by human influence 
on climate (high confidence). The estimated influence of natural forcing and internal 
variability on globally and annually averaged temperatures over that period is small (high 
confidence). (Ch. 3) 


• Natural variability, including El Nino events and other recurring patterns of ocean- 
atmosphere interactions, has important climate impacts on short time scales, but its 
influence is limited on global and regional climate trends over longer timescales (that is, a 
decade or more). (Very high confidence) (Ch. 1) 


• The average annual temperature of the contiguous United States has increased by about 
1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both show rapid warming 


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since the late 1970s, while paleo-temperature evidence shows that recent decades are the 
warmest in at least the past 1,500 years. (. High confidence) (Ch. 6) 


• For the contiguous United States, the largest temperature changes (from the average 
temperature in early 1900s compared to the average of the last 30 years) have occurred in 
the western United States, where average temperature increased by more than 1.5°F 
(0.8°C) across the Northwest and Southwest, and in the Northern Great Plains. ( Very high 
confidence ). (Ch. 6) 


Global Land and Ocean Temperature Anomalies 


Surface Temperature Trends 


Annual 




Change in Temperature (°F) 


Year -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 

Figure ES.l Global Temperatures Continue to Rise 

Left: Global annual average temperature has increased by more than 1.6°F (0.9°C) for the period 
1986-2015 relative to 1901-1960. Red bars show temperatures above the long-term 1880-2015 
average, and blue bars indicate temperatures below the long-term average. Right: Surface temperature 
trends (change in °F) for the period 1986-2015 relative to 1901-1960. From Figures 1.2. and 1.3 in 
Ch. 1. 

Projected Global and U.S. Temperature 

• Global climate is projected to continue to change over this century and beyond. Even if 
humans immediately ceased emitting greenhouse gases into the atmosphere, existing 
levels would commit the world to at least an additional 0.5°F (0.3°C) of warming over this 
century relative to today (high confidence). The magnitude of climate change beyond the 
next few decades depends primarily on the additional amount of greenhouse gases emitted 
globally, and on the sensitivity of Earth’s climate to those emissions (very high 
confidence). (Ch. 1, 4; Fig ES.2) 


C 

The average annual temperature of the contiguous United States is projected to 
continue to rise throughout the century. (Very high confidence). (Ch.6; Fig ES.3) 


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• For the United States, near-term increases of at least 2.5°F (1.4°C) are projected over the 
next few decades even under significantly reduced future emissions, meaning that the 
temperatures of recent record-setting years will become relatively common in the near 
future. Increases will be much larger by late century (5.0°F [2.8°C] under a scenario with 
lower emissions and 8.7°F [4.8°C] under a scenario with higher emissions). {High 
confidence) (Ch.6; Fig ES.3) 


C 

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ES.2 Figure ES.2. Greater Emissions Lead to Significantly More Warming 

The two panels above show projected changes in annual carbon emissions in units of gigatons 
of carbon (GtC) per year (left) and temperature change that would result from the central 
estimate (lines) and the likely ranges (shaded areas) of climate sensitivity (right). See the main 

report for more details on these scenarios and implications. Based on Figure 4.1 in Chapter 4 


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Projected Changes in Average Annual Temperature 



Mid 21st Century, Lower Emissions Mid 21st Century, Higher Emissions 


Late 21st Century, Lower Emissions 


Late 21st Century, Higher Emissions 



Change in Temperature (°F) 

1 I I I I I I I I 

2 4 6 8 10 12 14 16 18 


Figure ES.3 Significantly More Warming Occurs Under Higher Greenhouse Gas 
Concentrations 

This figure shows the projected changes in annual average temperature for mid- and late-21st 
century for various future pathways. Changes are the difference between the average for mid- 
century (2036-2065; top), late-century (2071-2100; bottom), and the average for near-present 
(1976-2005). See Figure 6 . 7 in Chapter 6 for more details. 


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Many Temperature and Precipitation Extremes Are Becoming More 
Common 


The increases in extreme weather that accompany global climate change are having 
significant, direct effects on the United States and the global economy and society. 
Temperature and precipitation extremes can affect water quality and availability, agricultural 
productivity, human health, vital infrastructure, iconic ecosystems and species, and the 
likelihood of disasters. Some extremes have already become more frequent, intense, or of 
longer duration, and many extremes are expected to continue to increase or worsen, 
presenting substantial challenges for built, agricultural, and natural systems. Some storm 
types such as hurricanes, tornadoes, and winter storms are also exhibiting changes that have 
been li nk ed to climate change, although detailed understanding of these linkages is still 
insufficient in the current state of the science. 


The frequency and intensity of heavy precipitation and extreme heat events are 
increasing in most regions of the world and will very likely continue to rise in the 
future. Trends for some other types of extreme events, such as floods, droughts, 
and severe storms, vary by region. ( Very high confidence ) (Ch.l) 


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• Extremely cold days have become warmer since the early 1900s, and extremely warm 
days have become wanner since the early 1960s. In recent decades, extreme cold waves 
have become less common while extreme heat waves have become more common. {Very 
high confidence ) (Ch. 6) 

• Heavy precipitation events across the United States have increased in both intensity and 
frequency since 1901. There are important regional differences in trends, with the largest 
increases occurring in the northeastern United States {high confidence ). The frequency 
and intensity of heavy precipitation events are projected to continue to increase over the 
century {high confidence). (Ch.7) 

• The frequency and severity of landfalling “atmospheric rivers” on the U.S. West Coast 
(narrow streams of moisture that account for 30%-40% of precipitation and snowpack in 
the region and are associated with severe flooding events) are projected to increase as a 
result of increasing evaporation and resulting higher atmospheric water vapor that occurs 
with increasing temperature. {Medium confidence) (Ch.9) 


• Recent droughts and associated heat waves have reached record intensities in some 

regions of the United States, but, by geographical scale and duration, the Dust Bowl era of 
the 1930s remains the benchmark drought and extreme heat event in the U.S. historical 
record. (Very high confidence) (Ch. 8) 


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• Reductions in western U.S. winter and spring snowpack are projected as the climate 
warms. Under higher-emissions scenarios, and assuming no change to current water- 
resources management, chronic, long-lasting, hydrological drought is possible by end of 
century. (Very high confidence ) (Ch. 8) 

• For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, 
increases are projected in precipitation rates ( high confidence) and intensity ( medium 
confidence). The frequency of the most intense storms is projected to increase in the 
Atlantic and western North Pacific ( low confidence) and in the eastern North Pacific 
(medium confidence). (Ch. 9) 


Observed Change 

in 5-year Extreme Precipitation Events 



Change (%) 



0-4 5-9 10-14 15+ 


Figure ES.4: Extreme Precipitation Has Increased Across the United States 

This figure shows the percentage difference between the 1901-1960 average and the 198 1— 
2015 average of the top 20% of the annual maximum daily precipitation values in each period 
for events exceeding the threshold for a 5 -year return period. The amount of precipitation 
falling in heavy events is greater across all regions in the entire contiguous United States. 
Based on figure 7.3 in Chapter 7. 


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Change in Coldest Temperature of the Year 
1986-2015 Avorago Minus 1901-1960 Average 


Change in Warmest Temperature of the Year 
1986-201 5 Average Minus 1901-1960 Average 



Difference (*F) 

• <-6 

• -610-4 

• -4 10-2 
•2 100 
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r i Tc- lwv * c"*? • 




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0K>2 

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• 4106 

• >6 




Figure ES.5 Extreme Cold Days Are Warming; Extreme Hot Days Dominated by 1930s 
Dust Bowl 

Observed changes in the coldest and warmest daily temperatures (in °F) of the year. Maps 
(top) depict changes at stations; changes are the difference between the average for present- 
day (1986-2015) and the average for the first half of the last century (1901-1960). Time 
series (bottom) depict changes averaged over the contiguous United States. Figure 6.3 from 
Chapter 6. 

****BOX ES.l 'k'k^k'k 

The Connected Climate System: Changes Halfway Across the World Are 
Affecting the United States 

Weather conditions and the ways they vary across regions and over the course of the year are 
influenced, in the United States as elsewhere, by a combination of fixed and variable factors, 
including local conditions (such as topography and urban heat islands), global trends (such as 
human-caused warming), and global and regional circulation patterns, including cyclical and 
chaotic patterns of natural variability within the climate system. For example, during an El 
Nino year, winters across the southwestern United States are typically wetter than average, 
and global temperatures are warmer than average. During a La Nina year, conditions across 
the southwestern United States are typically dry, and there tends to be a cooling effect on 
global temperatures. 


El Nino is not the only repeating pattern of natural variability in the climate system. Other 
important patterns include the North Atlantic Oscillation (NAO)/Northern Annular Mode 


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1 (NAM) that particularly affects conditions on the U.S. East Coast, and the North Pacific 

2 Oscillation (NPO) and Pacific North American Pattern (PNA) that especially affect conditions 

3 in Alaska and the U.S. West Coast, all of which are closely linked to other atmospheric 

4 circulation phenomena like the position of the jet streams. The influences of human activities 

5 on the climate system are now so pervasive that the current and future behavior of these 

6 previous “natural” climate features can no longer be assumed to be independent of those 

7 human influences. (Ch.5) 

8 Understanding the full scope of human impacts on climate requires a global focus because of 

9 the interconnected nature of the climate system. For example, the climate of the Arctic and the 

10 climate of the continental United States are strongly connected through atmospheric- 

1 1 circulation patterns. While the Arctic may seem physically remote to those living in other 

12 regions of the planet, the climatic effects of perturbations to Arctic sea ice, land ice, surface 

13 temperature, snow cover, and permafrost affect the amount of warming, sea level change, 

14 carbon cycle impacts, and potentially even weather patterns in the lower 48 states. The Arctic 

15 is wanning at a rate approximately twice as fast as the global average and, if it continues to 

16 wann at the same rate, Septembers will be nearly ice-free in the Arctic Ocean sometime 

17 between now and the 2040s. The important influence of Arctic climate change on Alaska is 

1 8 apparent; understanding the details of how climate change in the Arctic is affecting the 

19 climate in the continental United States is an area of active research. (Ch. 11) 

20 Changes in the tropics can also impact the rest of the globe, including the United States. There 

21 is growing evidence that the tropics have expanded over the past several decades, with an 

22 associated shift towards the poles of the subtropical dry zones in each hemisphere. The exact 

23 causes of this shift in the latitude of dry zones, and its implications, are not yet clear, although 

24 the shift is associated with projected drying of the American Southwest over the rest of the 

25 century. (Ch. 5) 


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Typical El Nino Winters 



Extended Pacific Jet 
Stream, amplified 
storm track 


Typical La Nina Winters 



Blocking 
high pressure 


Warmer 


Figure ES.6. Large-Scale Patterns of Natural Variability, Now Being Influenced by 
Human Activities, Affect U.S. Climate 

Typical January-March weather anomalies and atmospheric circulation during moderate to 
strong (top) El Nino, and (bottom) La Nina. From Figure 5.2 in Chapter 5. 


****END BOX ES.l**** 


Oceans Are Rising, Warming, and Becoming More Acidic 

Oceans occupy two thirds of the planet’s surface and host unique ecosystems and species, 
including those important for global commercial and subsistence fishing. Understanding 
climate impacts on the ocean and the ocean’s feedbacks to the climate system is critical for a 
comprehensive understanding of current and future changes in climate. 




More than 90% percent of the extra heat being trapped inside the climate system 
by human emissions is being absorbed by the ocean ( very high confidence), and 
the rate of acidification by uptake of CO 2 is faster than in at least the past 66 
million years ( medium confidence ). (Ch. 13) 


• Global mean sea level (GMSL) has risen by about 8-9 inches since 1880, with about 3 
inches of that rise occurring since 1990 (very high confidence) . Human-caused climate 


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1 change has made a substantial contribution to GMSL rise since 1900 (high confidence) , 

2 contributing to a rate of rise faster than during any comparable period for at least 2800 

3 years (medium confidence) . (Ch. 12; Fig ES.7) 

4 • Relative to the year 2000, GMSL is very likely to rise by 0.3-0. 6 feet by 2030; 0.5-1 .2 

5 feet by 2050; and 1-4 feet by 2100 (very high confidence in lower bounds of each of these 

6 predictions; medium confidence in upper bounds for 2030 and 2050; low confidence in 

7 upper bounds for 2100). (Ch. 12) 

8 • Differences in emissions trajectories over the next two decades (see Fig. ES.2) and beyond 

9 are estimated to have little effect on the projected amount of GMSL rise over the next few 

10 decades, but significantly affect how much more GMSL should be expected by the end of 

1 1 the century (high confidence) . Emerging scientific results regarding ice-sheet stability 

12 suggests that, under a higher scenario, a GMSL rise exceeding 8 feet by 2100 cannot be 

13 ruled out. (Ch. 12) 

14 • In most projections, GMSL will continue to rise beyond 2100 and even beyond 2200. The 

15 concept of a “sea level rise commitment” refers to the long-term projected sea level rise 

16 were the planet’s temperature stabilized at a given level. The paleo sea level record 

17 suggests that even 2°C (3.6°F) of warming above preindustrial global temperature may 

18 represent a commitment to six or more feet of rise ( high confidence ). (Ch. 12) 

19 • Relative sea level (RSL) rise in this century will vary along U.S. coastlines due to vertical 

20 land motion and changes in ocean circulation, as well as changes in Earth’s gravitational 

21 field and rotation from melting of land ice (very high confidence). For almost all future 

22 scenarios, RSL rise is likely to be greater than the global average in the U.S. Northeast and 

23 the western Gulf of Mexico. In intermediate and low scenarios, RSL rise is likely to be 

24 less than the global average in much of the Pacific Northwest and Alaska. For high 

25 scenarios, RSL rise is likely to be higher than the global average along all U.S. coastlines 

26 outside Alaska (high confidence). (Ch. 12) 

27 • Annual occurrences of daily tidal flooding — exceeding local thresholds for minor impacts 

28 to infrastructure — have increased 5- to 10-fold since the 1960s in several U.S. coastal 

29 cities (very high confidence). The changes in flood frequency over time are greatest where 

30 elevation is lower, local RSL rise is higher, or extreme variability is less (very high 

3 1 confidence). Tidal flooding will continue increasing in depth and frequency in similar 

32 manners this century (very high confidence). (Ch. 12; Fig. ES. 8) 

33 • The world’s oceans are currently absorbing about a quarter of the carbon dioxide emitted 

34 to the atmosphere annually from human activities (very high confidence) , making them 

35 more acidic with potential detrimental impacts to marine ecosystems. 

36 • The rate of acidification is unparalleled in at least the past 66 million years (medium 

37 confidence) . Acidification is regionally higher along U.S. coastal systems as a result of 


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changes in seasonal upwelling (for example, the Pacific Northwest and Alaska), changes 
in freshwater inputs (for example, the Gulf of Maine), and nutrient input (for example, in 
urbanized estuaries) ( medium confidence) . (Ch. 11; Ch.13) 

• Oxygen is essential to most life in the ocean, governing a host of biogeochemical and 
biological processes. Increasing sea surface temperatures, rising sea levels, and changing 
patterns of precipitation, winds, nutrients, and ocean circulation are all contributing to 
overall declining oxygen concentrations in ocean and coastal waters. Over the last half 
century, major oxygen losses have occurred in inland seas, estuaries, and in the coastal 
and open ocean. (High confidence) (Ch. 13) 

• By 2100, global-average ocean-oxygen levels are projected to decrease from current 
levels by 2%-4% relative to current levels for a range of scenarios. Much larger losses are 
projected in some regions and in different water masses. Potential effects on ocean 
ecosystems could be significant, but are not well understood. (Ch. 13) 




Figure ES.7 Recent Sea Level Rise Fastest for Over 2000 Years 

The top panel shows observed and reconstructed mean sea level for the last 2500 years. The 
bottom panel shows projected mean sea level for six future scenarios, including a risk-based 
high scenario that assumes major ice melting on portions of Antarctica. Based on Figure 12.1 
in Chapter 12. See the main report for more details. 


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Atlantic City, NJ 



1920 1940 1960 1980 2000 2020 2040 2060 2080 2100 


50 1 1 

T| , ■ ■ - 

1920 1940 1960 1980 2000 2020 


Figure ES. 8 “Nuisance Flooding” Increases Across the United States 

Annual occurrences of daily tidal flooding, also called sunny-day or nuisance flooding, have 
increased for some U.S. coastal cities. Examples shown above include Atlantic City, NJ; 
Baltimore, MD; Charleston, SC; Port Isabel, TX; La Jolla, CA; and San Francisco, CA. Based 
on data in Figure 12.3, Chapter 12. 

Climate Change in Alaska and across the Arctic Continues to Outpace 
Global Climate Change 

Residents of Alaska are on the front lines of climate change. Crumbling buildings, roads, 
bridges, and eroding shorelines are commonplace. Accelerated melting of multiyear sea ice 
cover, mass loss from the Greenland Ice Sheet, reduced snow cover, and permafrost thawing 
are stark examples of the rapid changes occurring in the Arctic. The climate system is 
connected (see Box ES.l), meaning that changes in the Arctic influence climate conditions 
outside the Artie. 

15 

f 

Alaska and Arctic surface and air temperatures are rising more than twice as 
fast as the global average. (Very high confidence) (Ch. 1 1) 
x - / 


17 


• Rising Alaskan temperatures are causing pennafrost to thaw and become more 

discontinuous; these changes lead to release of carbon dioxide and methane from the 
decomposition of previously frozen organic matter, adding to the global greenhouse gas 
forcing that is driving climate change. (High confidence ) (Ch.l 1) 


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1 • Losses of Arctic sea ice and Greenland Ice Sheet mass are accelerating, and Alaskan 

2 mountain glaciers continue to steadily melt ( very high confidence). Alaskan coastal sea- 

3 ice loss rates exceed the Arctic average ( very high confidence). Human activities have 

4 contributed to these reductions in sea and land ice (high confidence). 

5 • Observed sea- and land-ice losses across the Arctic are occurring faster than earlier 

6 climate models predicted (very high confidence). Melting trends are expected to continue 

7 with late summers becoming nearly ice-free for the Arctic Ocean by mid-century ( very 

8 high confidence). (Ch. 11) 

9 • Atmospheric circulation patterns connect the climates of the Arctic and the continental 

10 United States. The midlatitude circulation influences Arctic climate change ( medium-high 

1 1 confidence) . In turn, Arctic warming may be influencing midlatitude circulation over the 

12 continental United States, affecting weather patterns ( low-medium confidence). (Ch. 11) 

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Figure ES.9 Multiyear Sea Ice Has Declined Dramatically 

September sea ice extent and age (thickness) shown for 1984 (top) and 2016 (bottom), 
illustrating that significant reductions have occurred in sea ice extent and age. The bar graphs 
in the lower right of each panel illustrate the sea ice area covered within each age category. 
From Figure 11.1 in Chapter 11. 


Limiting Globally Averaged Warming to 2°C (3.6°F) Will Require a Major 
Reduction in Emissions 


Human activities are now the dominant cause of the observed changes in climate. For that 
reason, future climate projections are based on scenarios of how greenhouse gas emissions 
will continue to affect the climate over the remainder of this century and beyond. In 2016, 
significant steps were taken to limit future climate change in the form of three international 
agreements to reduce greenhouse-gas emissions: the Paris Agreement; an agreement to limit 
CO 2 emissions from aircraft under the International Civil Aviation Organization; and an 


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agreement to phase down hydrofluorocarbon (HFC) emissions under the Montreal Protocol 
(see Chapter 14 for more details on each). Despite the greenhouse-gas reductions planned 
under these agreements, there is still uncertainty about emissions due to changing economic, 
political, and demographic factors. For that reason, this report quantifies possible climate 
changes for a broad set of plausible future scenarios through the end of the century. (Chs. 4, 
14) 


A | 

Choices made today will determine the magnitude of climate change risks beyond 
the next few decades. (Chs. 4,14) 

v J 

9 

• There will be a delay of decades or longer between significant actions that reduce CO 2 
emissions and reductions in atmospheric CO 2 concentrations that contribute to surface 
warming. This delay — the result of the long lifetime of CO 2 in the atmosphere and the 
time delay in the response of the climate system to changes in the atmosphere — means 
that near-term changes in climate will be largely determined by past and present 
greenhouse gas emissions, modified by natural variability. ( Very high confidence) (Ch. 

14 ) 

• Limiting the global-mean temperature increase to 2°C (3.6°F) above preindustrial levels 
requires significant reductions in global CO 2 emissions relative to present-day emission 
rates. Cumulative emissions would likely have to stay below 1,000 gigatons carbon (GtC) 
for a 2°C objective, leaving about 400 GtC still to be emitted. Assuming future global 
emissions follow the RCP4.5 scenario (mid-low scenario in Fig ES.2), the total, 
cumulative emissions commensurate with the 2°C objective would likely be reached 
between 205 1 and 2065, while under the RCP8.5 scenario (higher scenario in Fig ES.2), 
this point would likely be reached between 2043 and 2050. {High confidence). (Ch 14) 

• If projected atmospheric CO 2 concentrations do not remain sufficiently low to prevent 2°C 
warming, climate-intervention strategies such as CO 2 removal or solar-radiation 
management could possibly offer additional means to limit or reduce temperature 
increases. Assessing the technical feasibility, costs, risks, co-benefits, and governance 
challenges of these additional measures, which are as-yet unproven at scale, would be of 
value to decision makers. {Medium confidence) (Ch. 14) 

• Atmospheric CO 2 levels have now passed 400 ppm, last seen during the Pliocene, 
approximately 3 million years ago, when global mean temperatures were 3.6° to 6.3°F (2° 
to 3.5°C) higher than preindustrial and sea levels were 66 ± 33 feet (20 ±10 meters) 
higher than today. {High confidence) (Ch. 4) 


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The observed acceleration in carbon emissions over the past 15-20 years is 
consistent with higher future scenarios {very high confidence ); since 2014, 
growth rates have slowed as economic growth begins to uncouple from 
carbon emissions {medium confidence) but not yet at a rate that would 
stabilize climate at either the 1.5° or 2°C Paris objectives (high confidence). 
(Ch. 4) 


• Continued growth in CO 2 emissions over this century and beyond would lead to 
concentrations not experienced in many millions of years. Present-day emissions rates of 
nearly 10 GtC per year, however, suggests that there is no precise past climate analogue 
for this century any time in at least the last 66 million years. {Medium confidence). {Ch A) 

There is a Significant Possibility for Unanticipated Changes 

Humanity is conducting an unprecedented experiment with the Earth’s climate system 
through emissions from large-scale fossil-fuel combustion, widespread deforestation, and 
other changes to the landscape. While scientists and policymakers rely on climate-model 
projections for a representative picture of the future Earth system under these conditions, 
there are still elements of the Earth system that models do not capture well. For this reason, 
there is significant potential for humankind’s planetary experiment to result in unanticipated 
surprises — and the further and faster the Earth’s climate system is changed, the greater the 
risk of such surprises. 

There are at least two types of potential surprises: compound events, where multiple extreme 
climate events occur simultaneously or sequentially (creating greater overall impact), and 
critical threshold or tipping point events, where some critical threshold is crossed in the 
climate system (that can lead to large impacts). The probability of such surprises, as well as 
other more predictable but difficult-to-manage impacts, increases as the influence of human 
activities on the climate system increases. (Ch. 15) 

( N \ 

Unanticipated changes are possible throughout the next century as tipping points 

are crossed and/or multiple climate-related extreme events occur simultaneously. 

(Ch. 15) 

\ ) 

• Self-reinforcing cycles, or positive feedbacks, in the climate system have the potential to 
substantially accelerate human-induced climate change and even shift the Earth’s climate 
system, in part or in whole, into new states that are very different from those experienced 
in the recent past — for example, ones with greatly diminished ice sheets or different large- 
scale patterns of atmosphere or ocean circulation. Some feedbacks and potential state 
shifts can be modeled and their probability of occurrence quantified; others can be 


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1 modeled or identified but not quantified; and some are probably still unknown (very high 

2 confidence) . (Ch. 2 and 15) 


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• The physical and socioeconomic impacts of a compound extreme event (such as 
simultaneous heat and drought, wildfires associated with hot and dry conditions, or 
flooding associated with high precipitation on top of snow or water-saturated ground) can 
be greater than the sum of those from individual extreme events (very high confidence) . 
Few analyses consider the spatial or temporal correlation between extreme events. (Ch. 

15) 

• Climate models are not yet able to include all of the processes that contribute to positive 
feedbacks, occurrence of extremes, and abrupt and/or irreversible changes. For this 
reason, future changes outside the range projected by current climate models cannot be 
ruled out ( very high confidence), and climate models are more likely to underestimate than 
to overestimate the amount of future change ( medium confidence ). (Ch. 15) 

****BOXES.2 ***** 

A Summary of What’s New Since NCA3 

A more detailed summary of what’s new since the release of the Third National Climate 
Assessment (NCA3) can be found at the end of Chapter 1, including the most notable 
advances in scientific understanding, new or improved tools and approaches, and changing 
context such as global-policy developments. 

New Understanding 

Detection and attribution : Significant advances have been made in the attribution of the 
human influence on individual climate and weather extreme events since NCA3. (Chapters 3, 
6, 7, 8). 

Atmospheric circulation and extreme events : The extent to which atmospheric circulation in 
the midlatitudes is changing or is projected to change is a new important area of research; this 
is particularly important for understanding changing extreme-climate conditions (Chapters 5, 
6, 7). 

Increased understanding of specific types of extreme events: The effects of climate change on 
specific types of extreme events in the United States is a key area where scientific 
understanding has advanced. (Chapter 9). 

The so-called global warming hiatus: Since NCA3, many studies have investigated causes for 
the temporary slowdown in the rate of increase in near-surface global mean temperature from 
2000 to 2013. This report provides a brief assessment of these studies. On the timescales 
relevant to human-induced climate change, the planet has continued to warm at a steady pace 


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Executive Summary 


1 as predicted by basic atmospheric physics and the well-documented buildup of heat-trapping 

2 gases in the atmosphere (Chapter 1). 

3 Oceans and coastal waters'. New research on ocean acidification, wanning, and oxygen loss is 

4 included in this report. There is also growing evidence that the Atlantic meridional 

5 overturning circulation (AMOC), sometimes referred to as the ocean’s conveyor belt, has 

6 slowed (Chapter 13). 

7 Local sea-level-change projections: For the first time in the NCA process, sea level rise 

8 projections incorporate geographic variation based on factors such as local land subsidence, 

9 ocean currents, and changes in Earth’s gravitational field (Chapter 12). 

10 Accelerated ice-sheet loss and irreversibility : New observations from many different sources 

1 1 confirm that ice-sheet loss is accelerating (Chapters 1,11, 12). 

12 Slowing of the regrowth of Arctic sea ice extent: The annual Arctic sea ice-extent minimum 

13 for 2016 was the second lowest on record. In fall 2016, record-setting, slow ice regrowth may 

14 lead to record-low values in 2016-2017 winter ice volume as well (Chapter 1 1). 

15 Potential surprises: Both large-scale state shifts in the climate system (sometimes 

16 called “tipping points”) and compound climate extremes (multiple simultaneous or sequential 

17 events) have the potential to generate unanticipated surprises. The discussion of these 

18 potential surprises in Chapter 15 marks the first extended treatment of this topic in an NCA 

19 report. (Chapter 15). 

20 Better Tools and Approaches 

21 Spatial downscaling: Modeled projections of climate changes are now statistically 

22 downscaled to a finer spatial resolution, generating temperature and precipitation predictions 

23 on a 1/16 degree latitude/longitude grid for the contiguous United States. (Chapters 4, 6, 7). 

24 Risk-based framing: Highlighting aspects of climate science most relevant to assessment of 

25 key societal risks is included more completely than in prior NCA assessments. 

26 Model weighting: For the first time, maps and plots of climate projections will use weighted 

27 averages of all available climate models. Individual model weights are based on their 1) 

28 historical perfonnance relative to observations and 2) independence relative to other models. 

29 (Chapters 4, 6, 7). 

30 High-resolution global climate model simulations: As computing resources have grown, more 

3 1 realistic simulations of intense weather systems, including hurricanes, are now possible. Even 

32 with the limited number of high-resolution models currently available, confidence has 

33 increased in projections of extreme weather (Chapter 9). 

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Changing Global Context 

The Paris Agreement : The COP21 Paris Agreement, which entered into force November 4, 
2016, provides a new framework for all nations to mitigate and adapt to climate change. The 
present document addresses the global climate implications of the agreement objectives 
(Chapter 4, 14). 

****End Box ES.2**** 


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l 1. Our Globally Changing Climate 


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KEY FINDINGS 

1 . The global climate continues to change rapidly compared to the pace of the natural 
changes in climate that have occurred throughout Earth’s history. Trends in globally- 
averaged temperature, sea-level rise, upper-ocean heat content, land-based ice melt, and 
other climate variables provide consistent evidence of a warming planet. These observed 
trends are robust, and have been confirmed by independent research groups around the 
world. ( Very high confidence) 

2. The frequency and intensity of heavy precipitation and extreme heat events are increasing 
in most regions of the world. These trends are consistent with expected physical 
responses to a wanning climate and with climate model studies, although models tend to 
underestimate the observed trends. The frequency and intensity of such extreme events 
will very likely continue to rise in the future. Trends for some other types of extreme 
events, such as floods, droughts, and severe stonns, have more regional characteristics. 

( Very high confidence ) 

3. Many lines of evidence demonstrate that human activities, especially emissions of 
greenhouse gases, are primarily responsible for the observed climate changes in the 
industrial era. There are no alternative explanations, and no natural cycles are found in 
the observational record that can explain the observed changes in climate. (Very high 
confidence) 

4. Global climate is projected to continue to change over this century and beyond. The 
magnitude of climate change beyond the next few decades depends primarily on the 
amount of greenhouse (heat trapping) gases emitted globally and the sensitivity of Earth’s 
climate to those emissions. ( Very high confidence) 

5. Natural variability, including El Nino events and other recurring patterns of 
ocean-atmosphere interactions, have important, but limited influences on global and 
regional climate over timescales ranging from months to decades. (Very high confidence) 

6. Longer-tenn climate records indicate that average temperatures in recent decades over 
much of the world have been much higher than at any time in the past 1700 years or 
more. (High confidence) 


31 1.1. Introduction 


32 Since the Third U.S. National Climate Assessment (NCA3) was published in May 2014, new 

33 observations along multiple lines of evidence have strengthened the conclusion that Earth’s 

34 climate is changing at a pace and in a pattern not explainable by natural influences. While this 


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1 report focuses especially on observed and projected future changes in the United States, it is 

2 important to understand those changes in the global context (this chapter). 

3 The world has warmed over the last 150 years, and that wanning has triggered many other 

4 changes to the Earth’s climate. Evidence for a changing climate abounds, from the top of the 

5 atmosphere to the depths of the oceans. Thousands of studies conducted by tens of thousands of 

6 scientists around the world have documented changes in surface, atmospheric, and oceanic 

7 temperatures; melting glaciers; disappearing snow cover; shrinking sea ice; rising sea level; and 

8 an increase in atmospheric water vapor. Rainfall patterns and storms are changing and the 

9 occurrence of droughts is shifting. 

10 Many lines of evidence demonstrate that human activities, especially emissions of greenhouse 

1 1 gases, are primarily responsible for the observed climate changes over the last 15 decades. There 

12 are no alternative explanations. There are no apparent natural cycles in the observational record 

13 that can explain the recent changes in climate (e.g., PAGES 2K 2013; Marcott et al. 2013). In 

14 addition, natural cycles within the Earth’s climate system can only redistribute heat; they cannot 

15 be responsible for the observed increase in the overall heat content of the climate system (Church 

16 et al. 201 1). Internal variability, alternative explanations, or even unknown forcing factors cannot 

17 explain the majority of the observed changes in climate (Anderson et al. 2012). The science 

18 underlying this evidence, along with the observed and projected changes in climate, is discussed 

19 in later chapters, starting with the basis for a human influence on climate in Chapter 2. 

20 Predicting how climate will change in future decades is a different scientific issue from 

21 predicting weather a few weeks from now. Local weather is short term and chaotic, and 

22 detennined by the complicated movement and interaction of high-pressure and low-pressure 

23 systems in the atmosphere, and thus it is difficult to predict day-to-day changes beyond about 

24 two weeks into the future. Climate, on the other hand, is the statistics of weather— meaning not 

25 just mean values but also the prevalence and intensity of extremes— as observed over a period of 

26 decades. Climate emerges from the interaction, over time, of rapidly and quite unpredictably 

27 changing local weather and more slowly changing and more predictable regional and global 

28 influences, such as the distribution of heat in the oceans, the amount of energy reaching Earth 

29 from the sun, and the composition of the atmosphere. 

30 Throughout this report, there are many new findings relative to those found in NCA3 and other 

3 1 assessments of the science. Several of these are highlighted in a “What’s New” box at the end of 

32 this chapter. 

33 1.2. The Globally Changing Climate 

34 1.2.1. Indicators of a Globally Changing Climate 

35 Highly diverse types of direct measurements made on land, sea, and in the atmosphere over 

36 many decades have allowed scientists to conclude with high confidence that global mean 


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temperature is increasing. Observational datasets for many other climate variables support the 
conclusion with high confidence that the global climate is changing. Figure 1.1 depicts several of 
the indicators that demonstrate trends consistent with a warming planet over the last century. 
Temperatures in the lower atmosphere and oceans have increased, as have near-surface humidity 
and sea level. Basic physics tells us that a warmer atmosphere can hold more water vapor; this is 
exactly what is measured from satellite data. At the same time, a warmer world means higher 
evaporation rates and major changes to the hydrological cycle, including increases in the 
prevalence of torrential downpours. In addition, Arctic sea ice, mountain glaciers, and Northern 
Hemisphere spring snow cover have all decreased. The relatively small increase in Antarctic sea 
ice in the last 15 years appears to be best explained a being due to localized natural variability 
(see e.g., Meehl et al. 2016). The vast majority of the glaciers in the world are losing mass at 
significant rates. The two largest ice sheets on our planet — Greenland and Antarctica — are 
shrinking. Five different observational datasets show the heat content of the oceans is increasing. 

Many other indicators of the changing climate have been detennined from other observations - 
for example, changes in the growing season and the allergy season (see e.g., 
https://www3.epa.gov/climatechange/science/indicators/; 

http://www.globalchange.gov/browse/indicators). In general, the indicators demonstrate 
continuing changes in climate since the publication of NCA3. As with temperature, independent 
researchers have analyzed each of these indicators and come to the same conclusion: all of these 
changes paint a consistent and compelling picture of a warming planet. 

[INSERT FIGURE 1.1 HERE: 

Figure 1.1. Examples of the observations from many different indicators of a changing climate. 
Anomalies are relative to 1976-2005 averages for the indicated variables. (Figure source: 
updated from Melillo et al. 2014). [Figure source: (top) adapted from NCEI 2016, (bottom) 
NOAA NCEI / CICS-NC] 

1.2.2. Trends in Global Temperatures 

Global annual average temperature (as measured over both land and oceans) has increased by 
more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960 (Figure 1.2). 
Global-average temperatures are not expected to increase smoothly in response to the human 
warming influences, because the warming trend is superimposed on natural variability associated 
with, e.g., the El Nino / La Nina ocean-heat oscillations and the cooling effects of particles 
emitted by volcanic eruptions. Even so, of the 16 wannest years in the ‘instrumental record’— the 
period, starting in the late 1800s, when coverage of thennometer measurements became adequate 
to calculate an global-average temperature for each year— 15 occurred in the period from 2001 to 
2015; and 2015 itself was the single wannest year in the entire instrumental record, eclipsing 
2014 by 0. 16°C (0.29°F), four times greater than the difference between 2014 and the next 
wannest year, 2010 (from NOAA data: http://www.ncdc.noaa.gov/cag/). As of November 2016, 
it appears that 2016 will eclipse 2015. According to NOAA’s temperature analyses, 2015 and 


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2014 were followed by 2010, 2013, 2005, 2009 and 1998 as the warmest years. Three of the four 
warmest years on record have occurred since the analyses through 2012 were reported in NCA3. 

A strong El Nino contributed to 2015’s record warmth (Blunden and Arndt 2016). It’s instructive 
to note, however, that the then-record global temperature of 1998, to which the previous, even 
more powerful El Nino contributed, was much lower than that of 2015. This fact indicates that 
the human warming influence, not El Nino per se, is the dominant factor producing new record- 
high temperatures. It must only be added that the El Nino / La Nina cycle itself can no longer be 
considered to be entirely ‘natural’; the human influence on Earth’s climate system is now so 
pervasive that we must assume that virtually all weather and climate phenomena are being 
affected in one way or another (Trenberth 2015). It is the complex interaction of natural sources 
of variability with the continuously growing human warming influence that is now shaping the 
Earth’s weather and climate. 

Globally, the trend over the past 50 years far exceeds what can be accounted for by natural 
variability alone (IPCC 2013). That does not mean, of course, that natural sources of variability 
have become insignificant. They can be expected to continue to contribute a degree of 
“bumpiness” in the year-to-year global-average temperature trajectory, as well as influences on 
the average rate of warming that can last as much as a decade or so (Karl et al. 2015; Deser et al. 
2012). For example, some combination of those natural sources of variability— and, perhaps, 
short- to medium-tenn changes in relation between human-caused warming and cooling effects— 
produced a much-discussed slowdown in the average pace of global wanning in the early 2000s 
(see Box 1.1).” 

[INSERT FIGURE 1.2 HERE: 

Figure 1.2. Top: Global annual average temperature (as measured over both land and oceans) 
has increased by more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960. 
Red bars show temperatures above the long-term 1880-2015 average, and blue bars indicate 
temperatures below the average over the entire period. While there is a clear long-term global 
warming trend, some years do not show a temperature increase relative to the previous year, and 
some years show greater changes than others. These year-to-year fluctuations in temperature are 
mainly due to natural sources of variability, such as the effects of El Ninos, La Ninas, and 
volcanic eruptions. Based on the NCEI (NOAAGlobalTemp) data set 1880-2015 (updated from 
Vose et al. 2012). Bottom: Global average temperature averaged over decadal periods ( 1 886— 
1895, 1896-1905, ..., 1996-2005, 2006-2015). Horizontal label indicates midpoint year of 

decadal period. Every decade since 1966-1975 has been wanner than the previous decade.] 


Warming during the first half of the 1900s occurred mostly in the Northern Hemisphere 
(Delworth and Knutson 2000). The last three decades have seen greater warming in response to 
accelerating increases in greenhouse gas concentrations, particularly at high northern latitudes, 
and over land as compared to the oceans (see Figure 1.3). In general, winter is warming faster 
than summer (especially in northern latitudes). Also, nights are warming faster than days 


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(Alexander et al. 2006, Davy et al. 2016). There is also some evidence of faster warming at 
higher elevations (Mountain Research Initiative 2015). 

A few regions, such as the North Atlantic Ocean, have experienced cooling over the last century, 
though these areas have warmed over recent decades. Regional climate variability is important 
(e.g., Hurrel and Deser 2009; Hoegh-Guldberg et al. 2014), but the effects of the increasing fresh 
water in the North Atlantic from melting of sea and land ice are also important (Rahmstorf et al. 
2015). Even in the absence of significant ice melt, we could expect the North Atlantic to wann 
more slowly given the larger heat capacity of the ocean, leading to land-ocean differences in 
warming. As a result, the climate for land areas often responds more rapidly than the ocean 
areas, even though the forcing driving a change in climate occurs equally over land and the 
oceans (IPCC 2013). 

[INSERT FIGURE 1.3 HERE: 

Figure 1.3. Surface temperature trends (change in °F) for the period 1986-2015 relative to 
1901-1960 from the National Centers for Environmental Information’s (NCEI) surface 
temperature product. The relatively coarse (5.0° x 5.0°) resolution of these maps does not capture 
IN finer details associated with mountains, coastlines, and other small-scale effects. (Figure 
source: updated from Vose et al. 2012).] 

Figure 1.4 shows the projected changes in globally averaged temperature for a range of future 
pathways that vary from assuming strong continued dependence on fossil fuels in energy and 
transportation systems over the 2 1 st century (the high scenario is Representative Concentration 
Pathway 8.5, or RCP8.5) to assuming major emission-reduction actions (the very low scenario, 
RCP2.6). Chapter 4 (Projections) describes the future scenarios and the models of the Earth’s 
climate system being used to quantify the impact of human choices and natural variability on 
future climate. Figure 1.4 suggests that global surface temperature increases for the end of the 
21st century are very likely to exceed 1.5°C (2.7°F) relative to the 1850-1900 average for all 
projections except for RCP2.6 (IPCC 2013). 

[INSERT FIGURE 1.4 HERE: 

Figure 1.4. Multimodel simulated time series from 1950 to 2100 for the change in global annual 
mean surface temperature relative to 1986-2005 for a range of future scenarios that account for 
the uncertainty in future emissions from human activities [as analyzed with the 20+ models from 
around the world used in the most recent international assessment (IPCC 2013)]. The mean and 

associated uncertainties [1.64 standard deviations (5%-95%) across the distribution of individual 

models (shading)] based on the averaged over 2081-2100 are given for all of the RCP scenarios 
as colored vertical bars. The numbers of models used to calculate the multimodel means are 
indicated. (Figure source: adapted from Walsh et al. 2014).] 

- — START BOX 1.1 HERE - — 


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Box 1.1. Was there a “Hiatus” in Global Warming? 

Over the past decade, there have been numerous assertions about a ‘hiatus’ (which means 
‘pause’) in global warming. These assertions are explored here in the context of long-term 
climate change. 

Statements about the hiatus often take the fonn of “there has been no global warming over the 
past X years,” where X is typically less than two decades. For relatively short periods of time, 
linear fits to the global mean temperature series can show zero or even slightly negative trends as 
a result of natural variability in the climate system (see Figure 1.5). However, since 1980, all 
periods exceeding 18 years (satellite data) or 13 years (surface data) have positive trends (Santer 
et al. 2016). In other words, surface and tropospheric temperature records do not support the 
assertion that long-term global wanning ceased (Lewandowsky et al. 2016), a conclusion further 
reinforced by recently updated and improved datasets (Karl et al. 2015; Mears and Wentz 2016; 
Richardson et al. 2016). 

[INSERT FIGURE 1.5 HERE: 

Figure 1.5. Panel A shows the annual mean temperature anomalies relative to a 1971-2000 
baseline for global mean surface temperature and global mean tropospheric temperature. A 
previous period of relatively slow-to-no warming (the “Big Hiatus”) is obvious from the mid- 
1940s to the mid-1970s. Panel B shows the linear trend of 17-year overlapping periods (the 
maximum number of years historically for less than positive trends), plotted at the time of the 
center of the trend period. During the recent slowdown period, warming only ceased for two 
versions of the satellite data, and for a very narrow range of time periods. All 17-year trends are 
increasing rapidly as the effects of the 2015-2016 El Nino-Southern Oscillation (ENSO) event 
begin to affect the trends. Panel C shows the annual mean Pacific Decadal Oscillation (PDO) 
index. Temperature trends show a marked tendency to be lower during periods of generally 
negative PDO index, shown by the blue shading. (Figure source: adapted and updated from 
Trenberth 2015 and Santer et al. 2016; Panel B, © American Meteorological Society. Used with 
permission.)] 

For the 15 years following the 1997-1998 ENSO event, the observed rate of warming was 
smaller than the underlying long-term increasing trend on 30-year climate time scales (Fyfe et al. 
2016). Variation in the rate of warming on this time scale is not unexpected and can be the result 
of long-term internal variability in the climate system, or short-term changes in climate forcings 
such as aerosols or solar irradiance. Temporary periods similar or larger in magnitude to the 
current slowdown have occurred earlier in the historical record; almost no increase occurred in 
the “Big Hiatus” occurred from the mid 1940s to the mid 1970s, which is understood to mostly 
be due to an increase in anthropogenic and volcanic aerosols during this period. Shorter-term 
slowdowns also occur after major volcanic eruptions, such as Pinatubo’s eruption in 1991. 
Temporary speedups have also occurred, most notably in the 1930s and early 1940s, and in the 
late 1970s and early 1980s. Comparable slowdown and speedup events are also present in 


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climate simulations of both historical and future climate, even without decadal scale fluctuations 
in forcing (Easterling and Wehner 2009), and thus the recent slowdown is not particularly 
surprising from a statistical point of view. 

Even though the slowdown of the early 2000s is not unexpected on statistical grounds, it has 
been used as informal evidence to cast doubt on the accuracy of climate projections from CMIP5 
models, since the measured rate of wanning in all surface and tropospheric temperature datasets 
from 2000-2015 was less than was expected given the results of the CMIP3 and CMIP5 
historical climate simulations (Fyfe et al. 2016; Santer et al. 2016). Thus it is important to 
explore a physical explanation of the recent slowdown and to identify the relative contributions 
of different factors. 

A number of studies have investigated the role of natural modes of variability and how they 
affected the flow of energy in the climate system of the post-2000 period (Balmaseda et al. 2013; 
England et al. 2014; Meehl et al. 2011; Kosaka and Xie 2013). For the 2000-2013 time period, 
they find: 

• In the Pacific Ocean, a number of interrelated features, including cooler than expected 
tropical ocean surface temperatures, stronger than nonnal trade winds, and a shift to the 
cool phase of the Pacific Decadal Oscillation (PDO) led to cooler than expected surface 
temperatures in the Eastern Tropical Pacific, a region that has been shown to have a 
strong influence on global-scale climate (Kosaka and Xie 2013). 

• For most of the world’s oceans, an excess amount of heat was transferred from the 
surface into the deeper ocean (Balmaseda et al. 2013; Chen and Tung 2014; Nieves et al. 
2015). The transfer of this heat to the deeper oceans removed heat from the atmosphere, 
causing a reduction in surface warming worldwide. 

• Other studies attributed part of the cause of the measurement/model discrepancy to 
natural fluctuations in radiative forcings, such as stratospheric water vapor, solar output, 
or volcanic aerosols (add Solomon et al. 2010; Schmidt et al 2014; (Huber and Knutti 
2014; Ridley et al. 2014; Santer et al. 2014). 

When comparing model predictions with measurements, it is important to note that the CMIP5 
runs used predicted values (not actual values) of these factors for time periods after 2000. Thus 
for these forcings, the model inputs were often different than what happened in the real-world, 
causing spurious warming in the model output. It is very likely that the early 2000s slowdown 
was caused by a combination of these factors, with natural internal variability in the world’s 
oceans being the dominant factor (Trenberth 2015). 

Although 2014 already set a new in globally averaged temperature record up to that time, in 
2015-2016, the situation changed dramatically. A switch of the PDO to the positive phase, 
combined with a strong El Nino event during the fall and winter of 2015-2016, led to months of 


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record-breaking globally averaged temperatures in both the surface and satellite temperature 
records (see Figure 1.5; Trenberth 2015). A plot of the trends in 17-year intervals shows a 
marked increase for trends ending over the past several years, suggesting that the slowdown may 
be over. 

On longer time scales, observed temperature changes are more consistent with model predictions 
and have been attributed to anthropogenic causes with high confidence (Bindoff et al. 2013). The 
pronounced globally averaged surface temperature record of 2015 appears to make recent 
observed temperature changes more consistent with model simulations — including with CMIP5 
projections that were (notably) developed in advance of occurrence of the 2015 observed 
anomalies (Figure 1.6). A second important point illustrated by Figure 1.6 is the broad overall 
agreement between observations and models on the century timescale, which is robust to the 
shorter-term variations in trends in the past decade or so. Continued global warming and the 
frequent setting of new high global mean temperature records or near-records is consistent with 
expectations based on model projections of continued anthropogenic forcing toward warmer 
global mean conditions. 

[INSERT FIGURE 1.6 HERE: 

Figure 1.6. Comparison of globally averaged temperature anomalies (°F) from observations 
(through 2015) and the CMIP5 multimodel ensemble (through 2016), using the reference period 
1961-1990. The CMIP5 multimodel ensemble (black) is constructed from blended surface 
temperature and surface air temperature data from the models, masked where observations are 
not available in the HadCRUT4 dataset (Knutson et al. 2016; see also Richardson et al. 2016). 
The sources for the three observational indices are: HadCRUT4.5 (red): 
http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html; NOAA (green): 
https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php; and GISTEMP (blue): 
http://data.giss. nasa.gov/gistemp/tabledata_v3/GLB. Ts+dSST.txt (all downloaded on Oct. 3, 
2016) (Figure source: adapted from Knutson et al. 2016).] 

- — END BOX 1.1 HERE- — 

1.2.3. Trends in Global Precipitation 

Annual averaged precipitation across global land areas exhibits a slight rise over the past century 
along with ongoing increases in atmospheric moisture levels (see Figure 1.7). Interannual and 
interdecadal variability is clearly found in all precipitation reconstructions, owing to factors such 
as the North Atlantic Oscillation (NAO) and ENSO — the latter accounting for record-low global 
totals in 2015 in several major analyses; note that precipitation reconstructions are updated 
operationally by NOAA NCEI on a monthly basis (Becker et al. 2013; Adler et al. 2003). 

[INSERT FIGURE 1.7 HERE: 

Figure 1.7. Surface annually-averaged precipitation trends (change in inches) for the period 
1986-2015 relative to 1901-1960. The relatively coarse (0.5° x 0.5°) resolution of these maps 


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does not capture the finer details associated with mountains, coastlines, and other small-scale 

effects. (Figure source: NOAA NCEI / CICS-NC).] 

The hydrological cycle and the amount of global mean precipitation is primarily controlled by 
energy budget considerations (Allen and Ingram 2002). The amount of global mean precipitation 
also changes as a result of a mix of fast and slow atmospheric responses to the changing climate 
(Collins et al. 2013). In the long term, increases in tropospheric radiative cooling due to CO 2 
increases must be balanced by increased latent heating, resulting in precipitation increases of 
approximately 1% to 3% per °C change (0.55% to 0.72% per °F) (IPCC 2013; Held and Soden 
2006). Changes in global atmospheric water vapor, on the other hand, are controlled by the 
Clausius-Clapeyron relationship (see Chapter 2: Science), increasing by about 6%-7% per °C of 
warming. Satellite observations of changes in precipitable water over ocean have been detected 
at about this rate and attributed to human changes in the atmosphere (Santer et al. 2007). Similar 
observed changes in land-based measurements have also been attributed to the changes in 
climate from greenhouse gases (Willet et al. 2010). 

Earlier studies suggested a pattern from climate change of wet areas getting wetter and dry areas 
getting dryer (e.g., Greve et al. 2014). While this behavior appears to be valid over ocean areas, 
changes over land are more complicated. The wet versus dry pattern in observed precipitation 
has only been attributed in a zonal mean sense (Zhang et al. 2007; Marvel and Bonfds 2013) due 
to the large amount of spatial variation in precipitation changes as well as significant natural 
variability. The detected signal in zonal mean is largest in the Northern Hemisphere, with 
decreases in the sub-tropics and increases at high latitudes. As a result, changes in annual 
averaged Arctic precipitation have been detected and attributed to human activities (Min et al. 
2008). 

1.2.4. Global Trends in Extreme Weather Events 

A change in the frequency, duration, and/or magnitude of extreme weather events is one of the 
most important consequences of a wanning climate. In statistical terms, a small shift in the mean 
of a weather variable occurring in concert with a change in the shape of its probability 
distribution can cause a large increase or decrease in the probability of a value above or below an 
extreme threshold (Katz and Brown 1992). Examples include extreme high-temperature events 
and heavy precipitation events. Additionally, extreme events such as intense tropical cyclones, 
mid-latitude cyclones, and hail and tornadoes associated with thunderstorms, can occur as 
isolated events that are not generally studied in terms of extremes within a probability 
distribution. Detecting trends in the frequency and intensity of extreme weather events is 
challenging (Sardeshmukh et al. 2015). The most intense events are rare by definition, and 
observations may be incomplete and suffer from reporting biases. Further discussion on trends 
and projections of extreme events for the United States can be found in Chapter 9: Extreme 
Storms. 


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1 Extreme Heat and Cold 

2 The frequency of multiday heat waves and extreme high temperatures at both daytime and 

3 nighttime hours is increasing over the United States (Meehl et al. 2009) and over much of the 

4 global land areas (IPCC 2013). The land area experiencing daily highs above given thresholds 

5 (for example, 90° F) has been increasing since about 1998 (Seneviratne et al. 2014). At the same 

6 time, frequencies of cold waves and daytime and nighttime extremely low temperatures are 

7 decreasing over the United States and much of the Earth (IPCC 2013; Easterling et al. 2016). 

8 The enhanced radiative forcing caused by greenhouse gases has a direct influence on heat 

9 extremes by shifting distributions of daily temperature (Min et al. 2013). Recent work indicates 

10 changes in atmospheric circulation may also play a significant role (See Chapter 5). For example, 

11 a recent study found that increasing anticyclonic circulations partially explain observed trends in 

12 heat events over North America and Eurasia, among other effects (Horton et al. 2015). Although 

13 the subject of significant study still, the observed changes in circulation may also be the result of 

14 human influences on climate. 

15 Extreme Precipitation 

16 A robust consequence of a wanning climate is an increase in atmospheric water vapor, which 

17 exacerbates precipitation events under similar meteorological conditions, meaning that when 

18 rainfall occurs, the amount of rain falling in that event tends to be greater. As a result, extreme 

19 precipitation events globally are becoming more frequent (IPCC 2013; Asadieh and Krakauer 

20 2015; Kunkel and Frankson 2015; Donat et al. 2016). On a global scale, the observational 

2 1 annual-maximum daily precipitation has increased by 8.5% over the last 110 years; global 

22 climate models also derive an increase in extreme precipitation globally but tend to 

23 underestimate the rate of the observed increase (Asadieh and Krakauer 2015; Donat et al. 2016). 

24 Extreme precipitation events are increasing globally in frequency over both wet and dry regions 

25 (Donat et al. 2016). Although more spatially heterogeneous than heat extremes, numerous 

26 studies have found increases in precipitation extremes on many regions using a variety of 

27 methods and threshold definitions (Kunkel et al. 2013), and those increases can be attributed to 

28 human-caused changes to the atmosphere (Min et al. 2011; Zhang et al., 2013). Finally, extreme 

29 precipitation associated with tropical cyclones (TCs) is expected to increase in the future 

30 (Knutson et al. 2015), but current trends are not clear (Kunkel et al. 2013). 

3 1 The impact of extreme precipitation trends on flooding globally is complex because additional 

32 factors like soil moisture and changes in land cover are important (Berghuijs et al. 2016). 

33 Globally, there is low confidence in current river-flooding trends (Kundzewicz et al. 2014), but 

34 the magnitude and intensity of river flooding is projected to increase in the future (Amell and 

35 Gosling 2014). More on flooding trends in the United States is in Chapter 8: Droughts, Floods, 

36 and Hydrology 

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1 Tornadoes and Thunderstorms 

2 Increasing air temperature and moisture increase the risk of extreme convection, and there is 

3 evidence for a global increase in severe thunderstonn conditions (Sander et al. 2013). Strong 

4 convection, along with wind shear, represents favorable conditions for tornadoes. Thus, there is 

5 reason to expect increased tornado frequency and intensity in a warming climate (Diffenbaugh et 

6 al. 2013). Inferring current changes in tornado activity is hampered by changes in reporting 

7 standards, and trends remain highly uncertain (Kunkel et al. 2013). 

8 Winter Storms 

9 Winter storm tracks have shifted slightly northward in recent decades over the Northern 

10 Hemisphere (Bender et al. 2012). More generally, extra-tropical cyclone (ETC) activity is 

1 1 projected to change in complex ways under future climate scenarios, with increases in some 

12 regions and seasons and decreases in others. There was good general agreement on these points 

13 among CMIP5 climate models, although some models underestimated the current cyclone track 

14 density (Colle et al. 2013; Chang 2013). 

15 Enhanced Arctic wanning (arctic amplification), due in part to sea ice loss, reduces lower 

16 tropospheric meridional temperature gradients, diminishing baroclinicity (a measure of how 

17 misaligned the gradient of pressure is from the gradient of air density) — an important energy 

18 source for ETCs. At the same time, upper-level meridional temperature gradients will increase, 

19 due to a wanning upper tropical troposphere and a cooling high-latitude lower stratosphere. 

20 While both effects counteract each other with respect to a projected change in mid-latitude storm 

2 1 tracks, the simulations indicate that the magnitude of arctic amplification is a controlling factor 

22 on circulation changes in the North Atlantic region (Barnes and Polvani 2015). 

23 Tropical Cyclones 

24 Detection of trends in past tropical cyclone activity is hampered by uncertainties in the data 

25 collected prior to the satellite era and by uncertainty in the relative contributions of natural 

26 variability and anthropogenic influences. Theoretical arguments and numerical modeling 

27 simulations support an expectation that radiative forcing by greenhouse gases and anthropogenic 

28 aerosols can affect tropical cyclone (TC) activity in a variety of ways, but robust formal 

29 detection and attribution for past observed changes has not yet been realized. Since the IPCC 

30 AR5 (2013), there is new evidence that the locations where TCs reach their peak intensity have 

3 1 migrated poleward in both the Northern and Southern Hemispheres, in concert with the 

32 independently measured expansion of the tropics (Kossin et al. 2014). In the western North 

33 Pacific, this migration has substantially changed the TC hazard exposure patterns in the region 

34 and appears to have occurred outside of the historically measured modes of regional natural 

35 variability (Kossin et al. 2016). 


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1 Whether global trends in high-intensity TCs are already observable is a topic of active debate. 

2 One study using the best-track data archive 1982-2009 found a significant positive global trend 

3 in lifetime maximum wind speed of high-intensity TCs, corroborating earlier work (Eisner et al. 

4 2008; Kossin et al. 2013). When the same procedure is applied to a homogenized satellite record 

5 over the same period, the trends are no longer significant. However, the same study also 

6 demonstrated that the observed changes in the environment are unlikely to support a detectable 

7 trend over that period (Kossin et al. 2013). Other studies have suggested that aerosol pollution 

8 has masked the increase in TC intensity expected otherwise from greenhouse warming (Wang et 

9 al. 2014; Sobel et al. 2016). 

10 TC intensities are expected to increase with warming, both on average and at the high end of the 

1 1 scale, as the range of achievable intensities expands, so that the most intense storms will exceed 

12 the intensity of any in the historical record (Sobel et al. 2016). Some studies have projected an 

13 overall increase in TC activity (Emanuel 2013). However studies with high-resolution models 

14 are giving a different result. For example, a high-resolution dynamical downscaling study of 

15 global TC activity under the RCP4.5 scenario projects an increased occurrence of the highest- 

16 intensity category TCs (Saffir-Simpson Categories 4 and 5), along with a reduced overall TC 

17 frequency, though there are considerable basin-to-basin differences (Knutson et al. 2015). 

18 Chapter 9 covers more on extreme storms affecting the United States. 

19 1.2.5. Global Changes in Land Processes 

20 Changes in land cover have had important effects on climate, while climate change also has 

21 important effects on land cover (IPCC 2013). In some case, there are changes in land cover that 

22 are both consequences of and influences on global climate change (e.g., declines in sea ice and 

23 snow cover, thawing permafrost). Other changes are currently mainly causes of climate change 

24 but in the future could become consequences (e.g., deforestation), while other changes are 

25 mainly consequences of climate change (e.g., effects of drought). 

26 Northern Hemisphere snow cover extent has decreased, especially in spring, primarily due to 

27 earlier spring snowmelt (Kunkel et al. 2016), and this decrease since the 1970s is at least 

28 partially driven by anthropogenic influences (Rupp et al. 2013). Snow cover reductions, 

29 especially in the Arctic region in summer, have led to reduced seasonal albedo. 

30 While global-scale trends in drought are uncertain due to lack of direct observations, regional 

3 1 trends indicate increased frequency and intensity of drought in the Mediterranean (Sousa et al. 

32 2011; Hoerling et al. 2013) and West Africa (Dai 2013; Sheffield et al. 2012), and decreased 

33 frequency and intensity in central North America (Peterson et al. 2013) and northwest Australia 

34 (Dai 2013; Sheffield et al. 2012; Jones et al. 2009). 

35 Anthropogenic land-use changes, such as deforestation and growing cropland extent, have 

36 increased the global land surface albedo, and a small amount of cooling can be attributed to this 

37 albedo change. Effects of other land use changes, including modifications of surface roughness, 


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1 latent heat flux, river runoff, and irrigation, are difficult to quantify, but may offset the direct 

2 land-use albedo changes (Bonan 2008; de Noble t-Ducoudre et al. 2012). 

3 Globally, land -use change since 1750 has been typified by deforestation, driven by the growth in 

4 intensive farming and urban development. Global land-use change is estimated to have released 

5 190 ± 65 PgC (petagrams of carbon) through 2014 (Le Quere et al. 2015). Over the same period, 

6 cumulative fossil fuel and industrial emissions are estimated to have been 405 ± 20 PgC, yielding 

7 total anthropogenic emissions of 590 ± 70 PgC, of which cumulative land-use change emissions 

8 were about 32% (Le Quere et al. 2015). Tropical deforestation is the dominant driver of land-use 

9 change emissions, estimated at 0. 1-1.7 PgC per year. Global deforestation emissions of about 3 

10 PgC per year are compensated by around 2 PgC per year of forest regrowth in some regions, 

1 1 mainly from abandoned agricultural land (Houghton et al. 2012; Pan et al. 2011). 

12 Natural terrestrial ecosystems are gaining carbon through uptake of CO 2 by enhanced 

13 photosynthesis due to higher CO 2 levels, increased nitrogen deposition, and longer growing 

14 seasons in mid- and high latitudes. Anthropogenic atmospheric CO 2 absorbed by land 

15 ecosystems is stored as organic matter in live biomass (leaves, stems, and roots), dead biomass 

16 (litter and woody debris), and soil carbon. 

17 Many studies have documented a lengthening growing (non-frozen) season, primarily due to the 

18 changing climate (Myneni et al. 1997; Pannesan and Yohe 2003; Menzel et al. 2006; Schwartz et 

19 al. 2006; Kim et al. 2012), and elevated CO 2 is expected to further lengthen the growing season 

20 (Reyes-Fox et al. 2014). In addition, a recent study has shown an overall increase in greening of 

21 the Earth in vegetated regions (Zhu et al. 2016), while another has demonstrated evidence that 

22 the greening of Northern Hemisphere extratropical vegetation is attributable to anthropogenic 

23 forcings, particularly rising atmospheric greenhouse gas levels (Mao et al. 2016). However, 

24 observations (Finzi et al. 2006; Palmroth et al. 2006; Norby et al. 2010) and models (Sokolov et 

25 al. 2008; Thornton et al. 2009; Zaehle and Friend 2010) indicate that nutrient limitations and 

26 land availability will constrain future land carbon si nk s. 

27 Modifications to the water, carbon, and biogeochemical cycles on land result in both positive and 

28 negative feedbacks to temperature increases (Betts et al. 2007; Bonan 2008; Bernier et al. 2011). 

29 Snow and ice albedo feedbacks are positive, leading to increased temperatures with loss of snow 

30 and ice extent. While land ecosystems are expected to have a net positive feedback due to 

3 1 reduced natural si nk s of CO 2 in a warmer world, anthropogenically increased nitrogen deposition 

32 may reduce the magnitude of the net feedback (Churkina et al. 2009; Zaehle et al. 2010; 

33 Thornton et al. 2009). Increased temperature and reduced precipitation increase wildfire risk and 

34 susceptibility of terrestrial ecosystems to pests and disease, with resulting feedbacks on carbon 

35 storage. Increased temperature and precipitation, particularly at high latitudes, drives up soil 

36 decomposition, which leads to increased CO 2 and CH 4 emissions (Page et al. 2002; Ciais et al. 

37 2005; Chambers et al. 2007; Kurz et al. 2008; Clark et al. 2010; van der Werf et al. 2010; Lewis 

38 et al. 2011). While some of these feedbacks are well known, others are not so well quantified and 


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1 yet others remain unknown; the potential for surprise is discussed further in Chapter 15: Potential 

2 Surprises. 

3 1.2.6. Global Changes in Sea Ice, Glaciers, and Land Ice 

4 Since NCA3 (Melillo et al. 2014), there have been significant advances in the understanding of 

5 changes in the cryosphere. Observations continue to show that Arctic sea ice extent and 

6 thickness, Northern Hemisphere snow cover, and the volume of mountain glaciers and 

7 continental ice sheets are all decreasing (Stroeve et al. 20 14a, b; Comiso and Hall 2014). In many 

8 cases, evidence suggests that the net loss of mass from the global cryosphere is accelerating 

9 (Rignot et al. 201 1, 2014; Williams et al. 2014; Zemp et al. 2015; Seo et al. 2015; Harig and 

10 Simons 2016). See Chapter 1 1 for more details on the Arctic and Alaska beyond the short 

1 1 discussion in this chapter. 

12 Arctic Sea Ice 

13 Arctic sea ice is a key component of the global climate system and appears to be in rapid 

14 transition. For example, sea-ice areal extent, thickness, and volume have been in decline since at 

15 least 1979 (IPCC 2013; Stroeve et al. 2014a, b; Comiso and Hall 2014), and annually averaged 

16 Arctic sea-ice extent has decreased since 1979 at a rate of 3.5%-4. 1% per decade (IPCC 2013; 

17 https://nsidc.org/arcticseaicenews/). Reductions in Arctic sea ice are found in all months and are 

18 most rapid in summer and autumn (Stroeve et al. 2012b; Stroeve et al. 2014a; Comiso and Hall 

19 2014). October 2016 was the slowest growth rate in Arctic sea ice in history for that month. 

20 Between 1979 and 2014, sea ice extent changes in March and September (the months of 

21 maximum and minimum extent) are -2.6% and -13.3% per decade, respectively (Perovich et al. 

22 2015). At the same time, the age distribution of sea ice has also become younger since 1988 

23 (Perovich et al. 2015). 

24 The rate of perennial and multiyear sea ice loss has been 11.5%±2.1% and 13.5% ±2.5% per 

25 decade, respectively, at the time of minimum extent (IPCC 2013). The thickness of the Arctic sea 

26 ice during winter has decreased between 1.3 and 2.3 meters (4 to 7.5 feet) (IPCC 2013). The 

27 length of the sea ice melt season has also increased by at least five days per decade since 1979 

28 for much of the Arctic (Stroeve et al. 2014a; Parkinson 2014). Lastly, current generation climate 

29 models still exhibit difficulties in simulating changes in Arctic sea ice characteristics, simulating 

30 weaker reductions in sea ice volume and extent than observed (IPCC 2013; Stroeve et al. 2012a; 

3 1 Stroeve et al. 2014b; Zhang and Knutson 2013). See Chapter 1 1 for further discussion of the 

32 implications of changes in the Arctic. 

33 Antarctic Sea Ice Extent 

34 The area of sea ice around Antarctica has increased between 1979 and 2012 by 1.2% to 1.8% per 

35 decade (IPCC 2013), much smaller than the decrease in total sea ice area found in the Arctic 

36 summer. Strong regional differences in the sea ice growth rates around Antarctica are found, but 


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1 most (about 75%) of the sea ice area has expanded over the last 30 years (Zunz et al. 2013; IPCC 

2 2013). Changes in wind patterns, ice-ocean feedbacks, and changes in freshwater flux have been 

3 investigated as contributing to the Antarctic sea ice growth, and there is still scientific debate 

4 around the physical cause (Zunz et al. 2013; Eisenman et al. 2014; Pauling et al. 2016). Scientific 

5 progress on understanding the observed changes in Antarctic sea ice extent is stymied by the 

6 short observational record; complex interactions between the sea ice, ocean, atmosphere, and 

7 Antarctic Ice Sheet; and large interannual variability. The most recent scientific evidence ties the 

8 increase in Antarctic sea ice extent to the negative phase of the Interdecadal Pacific Oscillation 

9 (IPO) climate variability pattern. The negative phase (1999-present) of the IPO resulted in cooler 

10 tropical Pacific sea surface temperatures, a slower warming trend, and a deepening of the 

1 1 Amundsen Sea Low near Antarctica, which contributed to regional circulation changes in the 

12 Ross Sea region and an expansion of sea ice (Meehl et al. 2016). 

13 Continental Ice Sheets and Mountain Glaciers 

14 Since the NCA3 (Mellilo et al. 2014), the Gravity Recovery and Climate Experiment (GRACE) 

15 constellation of satellites (e.g., Velicogna and Wahr 2013) has continued to provide a record of 

16 gravimetric measurements of land ice changes, advancing knowledge of recent mass loss to the 

17 global cryosphere. These measurements indicate that mass loss from the Antarctic Ice Sheet 

18 (AIS), Greenland Ice Sheet (GrIS), and mountain glaciers around the world continues. 

19 The annual average net mass change from AIS is -92 ± 10 Gt per year since 2003 (Harig and 

20 Simons 2016). Strong spatial variations are found in mass loss; gains are found in the East 

21 Antarctic Ice Sheet (EAIS), and significant losses are found in the West Antarctic Ice Sheet 

22 (WAIS). Multiple data sources indicate that losses from WAIS outpace the EAIS gains (Rignot 

23 et al. 2014; Joughin et al. 2014; Williams et al. 2014; Harig and Simons 2015; Seo et al. 2015; 

24 Harig and Simons 2016). The WAIS ice shelves are undergoing rapid change due to ocean 

25 wanning in this region from increased oceanic heat transport (Jenkins et al. 2010; Feldmann and 

26 Levermann 2015) contributing to the increase in flow rate of discharge glaciers. 

27 Recent evidence has found that the grounding line retreat of glaciers in the Amundsen Sea sector 

28 has crossed a threshold and this sector is expected to eventually disintegrate entirely, with the 

29 potential to destabilize the entire WAIS (Rignot et al. 2014; Joughin et al. 2014; Feldmann and 

30 Levermann 2015). As a result, the evidence suggests an eventual committed global sea level rise 

3 1 from this disintegration could be at least 1 .2 meters (about 4 feet) and possibly up to 3 meters 

32 (about 10 feet). The timescale over which the melt will occur is thought to be several centuries. 

33 However, recent analyses suggest that this could happen faster than previously thought, with a 

34 potential for an additional one or more feet of sea level rise during this century (DeConto and 

35 Pollard 2016; see Chapter 12: Sea Level Rise for further details). The potential for unanticipated 

36 rapid ice sheet melt and/or disintegration is discussed further in Chapter 15: Potential Surprises. 


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1 Average annual mass loss from GrIS between January 2003 and May 2013 was 244 ± 6 Gt per 

2 year (Harig and Simons 2016), an increase from 215 Gt per year for the period from 2002 to 

3 2011 (IPCC 2013). Major GrIS melting events have been observed in recent years associated 

4 with increased surface air temperatures in response to variability in the atmospheric circulation 

5 (IPCC 2013; Lim et al. 2016). GrIS is rapidly losing mass at its edges and slightly gaining in its 

6 interior and has been the largest land ice contributor to global sea level rise over the last decade 

7 (Harig and Simons 2012; Jacob et al. 2012). The surface area of the Greenland Ice Sheet 

8 experiencing melt has increased significantly since 1980 (Tedesco et al. 2011; Fettweis et al. 

9 2011; Tedesco et al. 2015). The Greenland surface melt recorded in 2012, where melt occurred 

10 over 98.6% of the ice sheet surface area on a single day in July, remains unprecedented (Nghiem 

11 et al. 2012; Tedesco et al. 2013). GRACE data indicate that the Greenland mass loss between 

12 April 2012 and April 2013 was 562 Gt — more than double the average annual rate found over 

13 recent decades. 

14 The annually averaged ice mass from 37 global reference glaciers has decreased every year since 

15 1984, and the rate of global glacier melt is accelerating (Pelto 2015; Zemp et al. 2015). This 

16 mountain glacier melt is contributing to sea level rise and will continue to contribute through the 

17 21st Century (Mengel et al. 2016). Some of the greatest glacier mass losses are occurring in 

18 Alaska and the Pacific Northwest (IPCC 2013; Zemp et al. 2015). The current data also show 

19 strong imbalances in glaciers around the globe indicating additional ice loss even if climate were 

20 to stabilize (IPCC 2013; Zemp et al. 2015). 

21 Arctic Snow Cover and Permafrost 

22 Snow cover extent has decreased in the Northern Hemisphere, including over the United States; 

23 the decrease has been especially significant over the last decade (Derksen and Brown 2012). 

24 Observations indicate that between 1967 and 2012, Northern Hemisphere June snow cover 

25 extent has decreased by more than 50% (IPCC 2013). Reductions in May and June snow cover 

26 extent of 7.3% and 19.8% per decade, respectively, have occurred over the period from 1979 to 

27 2014, while trends in snow cover duration show regions of both earlier and later snow cover 

28 onset (Derksen et al. 2015). 

29 Annual mean temperature and thickness of the active soil layer — the layer experiencing seasonal 

30 thaw — are critical permafrost characteristics for the concerns about potential emissions of carbon 

3 1 dioxide and methane from thawing permafrost. Permafrost temperatures have increased in most 

32 regions of the Arctic. The rate of pennafrost warming varies regionally; however, greater 

33 wanning is consistently found for colder permafrost than for warmer permafrost (IPCC 2013; 

34 Romanovsky et al. 2015). Decadal trends in the permafrost active layer show strong regional 

35 variability (Shiklomanov et al. 2012); however, the thickness of the active layer is increasing in 

36 most areas across the Arctic (IPCC 2013; Romanovsky et al. 2015). The potentially large 

37 contribution of carbon and methane emissions from permafrost and the continental shelf in the 

38 Arctic to overall warming is discussed further in Chapter 15: Potential Surprises. 


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1 1 . 2 . 7 . Global Changes in Sea Level 

2 Statistical analyses of tide-gauge data indicate that global mean sea level has risen about 20-23 

3 cm (8-9 inches) since 1880, with a rise rate of approximately 1.2-1. 5 mm/year from 1901-1990 

4 (~0. 5-0.6 inches per decade; Church and White 2011; Hay et al. 2015; also see Chapter 12: Sea 

5 Level Rise). However, since the early 1990s, both tide gauges and satellite altimeters have 

6 recorded a faster rate of sea level rise of about 3 mm/year (approximately 0.12 inches per year; 

7 Church and White 2011; Nerem et al. 2010; Hay et al. 2015), resulting in about 8 cm (about 3 

8 inches) of the global rise since the early 1990s. Nearly two-thirds of the sea level rise measured 

9 since 2005 has resulted from increases in ocean mass, primarily from land-based ice melt; the 

10 remaining one-third of the rise is in response to changes in density from increasing ocean 

1 1 temperatures (Merrifield et al. 2015). 

12 Global sea level rise and its regional variability forced by climatic and ocean circulation patterns 

13 are contributing to significant increases in annual tidal-flood frequencies, which are measured by 

14 NOAA tide gauges and associated with minor infrastructure impacts; along some portions of the 

15 U.S. coast, frequency of the impacts from such events appear to be accelerating (Ezer and 

16 Atkinson 2014; Sweet and Park 2014; also see Chapter 12: Sea-Level Rise). 

17 Future projections show that by 2100, global mean sea level is very likely to rise by 0.5-1 .3 m 

18 (1.6-4. 3 feet) under RCP8.5, 0.35-0.95 m (1. 1-3.1 feet) under RCP4.5, and 0.24-0.79 m (0.8— 

19 2.6 feet) under RCP2.6 (see Chapter 4: Projections of Climate Change for a description of the 

20 scenarios) (Kopp et al. 2014). Sea level will not rise uniformly around the coasts of the United 

2 1 States and its oversea territories. Local sea level rise is likely to be greater than the global 

22 average along the U.S. Atlantic and Gulf Coasts and less than the global average in most of the 

23 Pacific Northwest. Emerging science suggests these projections may be underestimates, 

24 particularly for higher scenarios; a global mean sea level rise exceeding 2.4 m (8 feet) by 2100 

25 cannot be excluded (see Chapter 12: Sea Level Rise), and even higher amounts are possible as a 

26 result of marine ice sheet instability (see Chapter 15: Potential Surprises). We have updated the 

27 global sea level rise scenarios for 2100 of Parris et al. (2012) accordingly (Sweet et al.. In Prep), 

28 and also extended to year 2200 in Chapter 12: Sea-Level Rise. The scenarios are regionalized to 

29 better match the decision context needed for local risk framing purposes. 

30 1 . 2 . 8 . Recent Global Changes relative to Paleoclimates 

3 1 Covering the last two millennia, referred to here as the “Common Era,” paleoclimate records 

32 provide a longer-term sample of the natural variability of modern climate, with a small overprint 

33 of human-forced climate change. The strongest drivers of climate in the last two thousand years 

34 have been volcanoes, land-use change (which has both albedo and greenhouse gas emissions 

35 effects), and emissions of greenhouse gases from fossil fuels and other human-related activities 

36 (Schmidt et al. 2011). Based on a number of proxies for temperature (for example, from tree 

37 rings, fossil pollen, corals, ocean and lake sediments, ice cores, etc.), temperature records are 


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available for the last 2000 years on hemispherical and continental scales (Figures 1.8 and 1.9) 
(Mann et al. 2008; PAGES 2K 2013). High-resolution temperature records for North America 
extend back less than half of this period, with temperatures in the early parts of the Common Era 
inferred from pollen archives. For this era, there is a general cooling trend, with a relatively rapid 
increase in temperature over the last 150-200 years (Figure 1.9, PAGES 2k 2013). For context, 
temperatures for 2015 are much higher than any period in the past 2000 years. 


[INSERT FIGURE 1.8 HERE: 

Figure 1.8. Changes in the temperature of the Northern Hemisphere from surface observations 
(in red) and from proxies (in black; uncertainty range represented by shading) relative to 1 961— 
1990 average temperature. These analyses suggest that current temperatures are higher than seen 
globally in at least the last 1700 years, and that the last decade (2006 to 2015) was the warmest 
decade on record. (Figure source: adapted and updated from Mann et al. 2008).] 

[INSERT FIGURE 1.9 HERE: 

Figure 1.9. Proxy temperatures reconstructions for the seven regions of the PAGES-2K 
Network. Temperature anomalies are relative to the 1961-1990 reference period. Gray lines 
around expected-value estimates indicate uncertainty ranges as defined by each regional group 
(see PAGE 2K 2013 and related Supplementary Information). Note that the changes in 
temperature over the last century tend to occur at a much faster rate than found in the previous 
time periods. (Figure source: adapted from PAGES 2k et al. 2013)] 

Global temperatures of the magnitude observed recently (and projected for the rest of this 
century) were likely last observed during the Eemian period — the last interglacial — 125,000 
years ago; at that time, global temperatures were, at their peak, about 1.8°-3.6°F (1°-2°C) 
warmer than preindustrial temperatures (Turney and Jones 2010). Coincident with these higher 
temperatures, sea levels were 6-9 meters (about 16-30 feet) higher than modem levels (Kopp et 
al. 2009; Dutton and Lambeck 2012). Modeling studies suggest that the Eemian period warming 
can be explained in part by increased solar insolation from orbital forcing as the Earth travels 
around the Sun (e.g., Kaspar et al. 2005). However, greenhouse gas concentrations were similar 
to preindustrial levels. Equilibrium climate with modern greenhouse gas concentrations (about 
400 ppm CO 2 ) most recently occurred 3 million years ago during the Pliocene. During the 
wannest parts of this period, global temperatures were 5.4°-7.2°F (3°-4°C) higher than today, 
and sea levels were 25 meters (about 82 feet) higher (Haywood et al. 2013). 


START BOX 1.2 HERE 


Box 1.2: What’s New in This Report 

This assessment reflects both advances in scientific understanding and approach since NCA3, as 
well as global policy developments. Highlights of what’s new include: 


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1 Spatial downscaling : Projections of climate changes are downscaled to a finer resolution than the 

2 original global climate models using the Localized Constructed Analogs (LOCA) empirical 

3 statistical downscaling model. The downscaling generates temperature and precipitation on a 

4 1/16 degree latitude/longitude grid for the contiguous United States (Chapters 4,6,7). 

5 Risk-based framing : Highlighting aspects of climate science most relevant to assessment of key 

6 societal risks is included more here than in prior NCA assessments. This approach allows for 

7 emphasis of possible outcomes that, while relatively unlikely to occur or characterized by high 

8 uncertainty, would be particularly consequential, and thus associated with large risks. 

9 Detection and attribution : Significant advances have been made in the attribution of the human 

10 influence on individual climate and weather extreme events since NCA3. This assessment 

1 1 contains extensive discussion of new and emerging findings in this area (Chapters 3, 6, 7, 8). 

12 Atmospheric circulation and extreme events: The extent to which atmospheric circulation in the 

13 mid latitudes is changing or is projected to change, possibly in ways not captured by current 

14 climate models, is a new important area of research. While still in its formative stages, this 

15 research is critically important because of the implications of such changes for climate extremes 

16 including extended cold air outbreaks, long-duration heat waves, and changes in storms and 

17 drought patterns (Chapters 5,6,7). 

1 8 Increased understanding of specific types of extreme events: How climate change may affect 

19 specific types of extreme events in the United States is another key area where scientific 

20 understanding has advanced. For example, this report highlights how intense flooding associated 

2 1 with atmospheric rivers could increase dramatically as the atmosphere and oceans warm, or how 

22 tornadoes could be concentrated into a smaller number of high-impact days (Chapter 9). 

23 Model weighting : For the first time, maps and plots of climate projections will not show a 

24 straight average of all available climate models. Rather, each model is given a weight based on 

25 their 1) historical perfonnance relative to observations and 2) independence relative to other 

26 models. Although this is a more accurate way of representing model output, it does not 

27 significantly alter the results: the weighting produces very similar trends and spatial patterns to 

28 the equal-weighting-of-models approach used in prior assessments (Chapters 4,6,7). 

29 High-resolution global climate model simulations'. As computing resources have grown, 

30 multidecadal simulations of global climate models are now being conducted at horizontal 

3 1 resolutions on the order of 25 km (15 miles) that enable more realistic simulation of intense 

32 weather systems, including hurricanes. Even the limited number of high-resolution models 

33 currently available have increased confidence in projections of extreme weather (Chapter 9). 

34 The so-called global warming hiatus : Since NCA3, many studies have investigated causes for 

35 the temporary slowdown in the rate of increase in near-surface global mean temperature from 

36 2000 to 2013. The slowdown, which ended with the record warmth in 2014-2016, is understood 

37 to have been caused by a combination of internal variability, mostly in the heat exchange 

38 between the ocean and the atmosphere, and short-term variations in external forcing factors, both 


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2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

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23 

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Chapter 1 


human and natural. On longer time scales relevant to human-induced climate change, the planet 
continues to warm at a steady pace as predicted by basic atmospheric physics and the well- 
documented increase in heat-trapping gases 

Oceans and coastal waters : Concern over ocean acidification, warming, and oxygen loss is 
increasing as scientific understanding of the severity of their impacts grows. Both acidification 
and oxygen decreases may be magnified in some U.S. coastal waters relative to the global 
average, raising the risk of serious ecological and economic consequences. There is also growing 
evidence that the Atlantic Meridional Circulation (AMOC), sometimes referred to as the ocean’s 
conveyor belt, has slowed down (Chapter 13). 

Local sea-level change projections : For the first time in the NCA process, sea-level rise 
projections incorporate geographic variation based on factors such as local land subsidence, 
ocean currents, and changes in Earth’s gravitational field (Chapter 12). 

Accelerated ice-sheet loss and irreversibility. New observations from many different sources 
confirm that ice-sheet loss is accelerating. Combined with simultaneous advances in the physical 
understanding of ice sheets, scientists are now concluding that up to 8.5 feet of global sea-level 
rise is possible by 2100 under a high-emissions scenario, up from 6.6 feet in NCA3 (Chapter 12). 

Slowing in Arctic sea-ice area extent regrowth: The annual Arctic sea ice extent minimum for 

2016 was the second lowest on record. In fall 2016, record-setting slow ice regrowth put 2016- 

2017 winter ice volume records in jeopardy as well (Chapter 1 1). 

Potential surprises'. Both large-scale state shifts in the climate system (sometimes called “tipping 
points”) and compound extremes have the potential to generate unanticipated surprises. The 
further the earth system departs from historical climate forcings, and the more the climate 
changes, the greater the potential for these surprises. For the first time in the NCA process we 
include an extended discussion of these potential surprises (Chapter 15). 

The Paris Agreement : The Paris Agreement, which entered into force November 4, 2016, 
provides a new framework for all nations to mitigate and adapt to climate change, and to 
periodically update and revisit their respective domestic commitments. The Agreement’s long- 
term objective is to limit average global temperature change to well below 2°C (3.6°F) above 
preindustrial levels, with best efforts to limit it to(1.5°C (2.7°F), and this assessment addresses 
global climate implications of these objectives (Chapter 4, 14). 

END BOX 1.2 HERE 


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Chapter 1 


1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 The global climate continues to change rapidly compared to the pace of the natural changes in 

4 climate that have occurred throughout Earth’s history. Trends in globally-averaged temperature, 

5 sea-level rise, upper-ocean heat content, land-based ice melt, and other climate variables provide 

6 consistent evidence of a warming planet. These observed trends are robust, and have been 

7 confirmed by independent research groups around the world. 

8 Description of evidence base 

9 The Key Finding and supporting text summarize extensive evidence documented in the climate 

10 science literature. Similar to statements made in previous national (NCA3; Melillo et al. 2014) 

1 1 and international (IPCC 2013) assessments. 

12 Evidence for changes in global climate arises from multiple analyses of data from in-situ, 

13 satellite, and other records undertaken by many groups over several decades. These observational 

14 datasets are used throughout this chapter and are discussed further in Appendix 1 (e.g., updates 

15 of prior uses of these datasets by Vose et al. 2012; Karl et al. 2015). Changes in the mean state 

16 have been accompanied by changes in the frequency and nature of extreme events (e.g., Kunkel 

17 and Fra nk son 2015; Donat et al. 2016). A substantial body of analysis comparing the observed 

1 8 changes to a broad range of climate simulations consistently points to the necessity of invoking 

19 human-caused changes to adequately explain the observed climate system behavior. The 

20 influence of human impacts on the climate system has also been observed in a number of 

21 individual climate variables (attribution studies are discussed in Chapter 3 and in other chapters). 

22 Major uncertainties 

23 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

24 particularly regional, scales, and especially for extreme events and our ability to simulate and 

25 attribute such changes using climate models. Innovative new approaches to climate data analysis, 

26 continued improvements in climate modeling, and instigation and maintenance of reference 

27 quality observation networks such as the U.S. Climate Reference Network 

28 (http://www.ncei.noaa.gov/crn/) all have the potential to reduce uncertainties. 

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement 

31 x Very High 

32 □ High 

33 □ Medium 

34 □ Low 

35 There is very high confidence that global climate is changing and this change is apparent across a 

36 wide range of observations, given the evidence base and remaining uncertainties. All 


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Chapter 1 


1 observational evidence is consistent with a warming climate since the late 1800s. There is very 

2 high confidence that the global climate change of the past 50 years is primarily due to human 

3 activities, given the evidence base and remaining uncertainties (IPCC 2013). Recent changes 

4 have been consistently attributed in large part to human factors across a very broad range of 

5 climate system characteristics. 

6 Summary sentence or paragraph that integrates the above information 

7 The key message and supporting text summarizes extensive evidence documented in the climate 

8 science peer-reviewed literature. The trends described in NCA3 have continued and our 

9 understanding of the observations related to climate and the ability to evaluate the many facets of 

10 the climate system have increased substantially. 

11 

12 Key Finding 2 

13 The frequency and intensity of heavy precipitation and extreme heat events are increasing in 

14 most regions of the world. These trends are consistent with expected physical responses to a 

15 wanning climate and with climate model studies, although models tend to underestimate the 

16 observed trends. The frequency and intensity of such extreme events will very likely continue to 

17 rise in the future. Trends for some other types of extreme events, such as floods, droughts, and 

18 severe storms, have more regional characteristics. 

19 Description of evidence base 

20 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

21 science literature and are similar to statements made in previous national (NCA3; Melillo et ah, 

22 2014) and international (IPCC 2013) assessments. The analyses of past trends and future 

23 projections in extreme events are also well substantiated through more recent peer review 

24 literature as well (Seneviratne et al. 2014; Easterling et al. 2016; Kunkel and Frankson 2015; 

25 Donat et al. 2016; Berghuijs et al. 2016; Arnell and Gosling 2014). 

26 Major uncertainties 

27 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

28 particularly regional, scales, and especially for extreme events and our ability to simulate and 

29 attribute such changes using climate models. Innovative new approaches to climate data analysis, 

30 continued improvements in climate modeling, and instigation and maintenance of reference 

3 1 quality observation networks such as the U.S. Climate Reference Network 

32 (http://www.ncei.noaa.gov/crn/) all have the potential to reduce uncertainties. 

33 Assessment of confidence based on evidence and agreement, including short description of 

34 nature of evidence and level of agreement 

35 x Very High 

36 □ High 

37 □ Medium 


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1 □ Low 

2 There is very high confidence, based on the observational evidence and physical understanding, 

3 that there are major trends in extreme events and significant projected changes for the future. 

4 Summary sentence or paragraph that integrates the above information 

5 The key message and supporting text summarizes extensive evidence documented in the climate 

6 science peer-reviewed literature. The trends for extreme events that were described in the NCA3 

7 and IPCC assessments have continued and our understanding of the data and ability to evaluate 

8 the many facets of the climate system have increased substantially. 

9 

10 Key Finding 3 

1 1 Many lines of evidence demonstrate that human activities, especially emissions of greenhouse 

12 gases, are primarily responsible for the observed climate changes in the industrial era. There are 

13 no alternative explanations, and no natural cycles are found in the observational record that can 

14 explain the observed changes in climate. 

15 Description of evidence base 

16 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

17 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

18 2014) and international (IPCC 2013) assessments. The human effects on climate have been well 

19 documented through many papers in the peer reviewed scientific literature (e.g., see Chapters 2 

20 and 3 for more discussion of supporting evidence). 

21 Major uncertainties 

22 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

23 particularly regional, scales, and especially for extreme events and our ability to simulate and 

24 attribute such changes using climate models. The exact effects from land use changes relative to 

25 the effects from greenhouse gas emissions needs to be better understood. 

26 Assessment of confidence based on evidence and agreement, including short description of 

27 nature of evidence and level of agreement 

28 x Very High 

29 □ High 

30 □ Medium 

31 □ Low 

32 There is very high confidence for a major human influence on climate. 

33 Summary sentence or paragraph that integrates the above information 

34 The key message and supporting text summarizes extensive evidence documented in the climate 

35 science peer-reviewed literature. The analyses described in the NCA3 and IPCC assessments 


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Chapter 1 


1 support our findings and new observations and modeling studies have further substantiated these 

2 conclusions. 

3 

4 Key Finding 4 

5 Global climate is projected to continue to change over this century and beyond. The magnitude 

6 of climate change beyond the next few decades depends primarily on the amount of greenhouse 

7 (heat trapping) gases emitted globally and the sensitivity of Earth’s climate to those emissions. 

8 Description of evidence base 

9 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

10 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

1 1 2014) and international (IPCC 2013) assessments. The projections for future climate have been 

12 well documented through many papers in the peer reviewed scientific literature (e.g., see Chapter 

13 4 for descriptions of the scenarios and the models used). 

14 Major uncertainties 

15 Key remaining uncertainties relate to the precise magnitude and nature of changes at global, and 

16 particularly regional, scales, and especially for extreme events and our ability to simulate and 

17 attribute such changes using climate models. Continued improvements in climate modeling to 

18 represent the physical processes affecting the Earth’s climate system are aimed at reducing 

19 uncertainties. Monitoring and observation programs also can help improve the understanding 

20 needed to reduce uncertainties. 

21 Assessment of confidence based on evidence and agreement, including short description of 

22 nature of evidence and level of agreement 

23 x Very High 

24 □ High 

25 □ Medium 

26 □ Low 

27 There is very high confidence for continued changes in climate. 

28 Summary sentence or paragraph that integrates the above information 

29 The key message and supporting text summarizes extensive evidence documented in the climate 

30 science peer-reviewed literature. The projections that were described in the NCA3 and IPCC 

3 1 assessments support our findings and new modeling studies have further substantiated these 

32 conclusions. 

33 

34 


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1 Key Finding 5 

2 Natural variability, including El Nino events and other recurring patterns of ocean-atmosphere 

3 interactions, have important, but limited influences on global and regional climate over 

4 timescales ranging from months to decades. 

5 Description of evidence base 

6 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

7 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

8 2014) and international (IPCC 2013) assessments. The role of natural variability in climate 

9 trends has been extensively discussed in the peer reviewed literature (e.g., Karl et al. 2015; 

10 Rahmstorf et al. 2015; Lewandowsky et al. 2016; Mears and Wentz 2016; Trenberth et al. 2014; 

11 Santer et al. 2016). 

12 Major uncertainties 

13 Uncertainties still exist in the precise magnitude and nature of the full effects of individual ocean 

14 cycles and other aspects of natural variability on the climate system. Increased emphasis on 

15 monitoring should reduce this uncertainty significantly over the next few decades. 

16 Assessment of confidence based on evidence and agreement, including short description of 

17 nature of evidence and level of agreement 

18 x Very High 

19 □ High 

20 □ Medium 

21 □ Low 

22 There is very high confidence, affected to some degree by limitations in the observational record, 

23 that the role of natural variability on future climate change is limited. 

24 Summary sentence or paragraph that integrates the above information 

25 The key message and supporting text summarizes extensive evidence documented in the climate 

26 science peer-reviewed literature. There has been an extensive increase in the understanding of 

27 the role of natural variability on the climate system over the last few decades, including a 

28 number of new findings since NCA3. 

29 

30 Key Finding 6 

3 1 Longer-tenn climate records indicate that average temperatures in recent decades over much of 

32 the world have been much higher than at any time in the past 1700 years or more. 

33 Description of evidence base 

34 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

35 science literature and are similar to statements made in previous national (NCA3; Melillo et al., 

36 2014) and international (IPCC 2013) assessments. There are many recent studies of the 


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Chapter 1 


1 paleocliamte leading to this conclusion including those cited in the report (e.g., Mann et al. 2008; 

2 PAGE 2K 2013). 

3 Major uncertainties 

4 Despite the extensive increase in knowledge in the last few decades, there are still many 

5 uncertainties in understanding the hemispheric and global changes in climate over the Earth’s 

6 history, including that of the last few millennia. Additional research efforts in this direction can 

7 help reduce those uncertainties. 

8 Assessment of confidence based on evidence and agreement, including short description of 

9 nature of evidence and level of agreement 

10 □ Very High 

1 1 x High 

12 □ Medium 

13 □ Low 

14 There is high confidence for current temperatures to be higher than they have been in at least 

15 1700 years and perhaps much longer. 

16 Summary sentence or paragraph that integrates the above information 

17 The key message and supporting text summarizes extensive evidence documented in the climate 

18 science peer-reviewed literature. There has been an extensive increase in the understanding of 

19 past climates on our planet, including a number of new findings since NCA3. 

20 


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Chapter 1 


1 FIGURES 



2 

3 

4 

5 

6 



Figure 1.1. Examples of the observations from many different indicators of a changing climate. 
Anomalies are relative to 1976-2005 averages for the indicated variables. (Figure source: 
updated from Melillo et al. 2014). 


58 


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2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 


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Chapter 1 


Global Land and Ocean Temperature Anomalies 

Annual 



Decadal 



1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 
Decade 


Figure 1.2. Top: Global annual average temperature (as measured over both land and oceans) 
has increased by more than 1.6°F (0.9°C) for the period from 1986-2015 relative to 1901-1960. 
Red bars show temperatures above the long-term 1880-2015 average, and blue bars indicate 
temperatures below the average over the entire period. While there is a clear long-term global 
warming trend, some years do not show a temperature increase relative to the previous year, and 
some years show greater changes than others. These year-to-year fluctuations in temperature are 
mainly due to natural sources of variability, such as the effects of El Ninos, La Ninas, and 
volcanic eruptions. Based on the NCEI (NOAAGlobalTemp) data set 1901-2015 (updated from 
Vose et al. 2012). Bottom: Global average temperature averaged over decadal periods ( 1 886— 
1895, 1896-1905, ..., 1996-2005, 2006-2015). Horizontal label indicates mid-point year of 
decadal period. Every decade since 1966-1975 has been wanner than the previous decade. 
(Figure source: (top) adapted from NCEI 2016, (bottom) NOAA NCEI / CICS-NC) 


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Chapter 1 


Surface Temperature Trends 



Change in Temperature (°F) 


1 - 1.5 - 1.0 - 0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 

2 

3 Figure 1.3. Surface temperature trends (change in °F) for the period 1986-2015 relative to 

4 1901-1960 from the NOAA National Centers for Environmental Information’s (NCEI) surface 

5 temperature product. The relatively coarse (5.0° x 5.0°) resolution of these maps does not capture 

6 the finer details associated with mountains, coastlines, and other small-scale effects. (Figure 

7 source: updated from Vose et al. 2012). 

8 



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2 

3 

4 

5 

6 

7 

8 

9 


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CD 

CD 

C 

03 


o 



12 

10 

8 

6 

4 

2 

0 



Observations 

Modeled Historical 

RCP8.5 

RCP2.6 


-2 

1900 1950 2000 2050 

Year 


2100 


10 

00 

0. 

O 

(T 


Figure 1.4. Multimodel simulated time series from 1950 to 2100 for the change in global annual 
mean surface temperature relative to 1986-2005 for a range of future emissions scenarios that 
account for the uncertainty in future emissions from human activities [as analyzed with the 20+ 
models from around the world used in the most recent international assessment (IPCC 2013)]. 
The mean and associated uncertainties [1.64 standard deviations (5%— 95%) across the 
distribution of individual models (shading)] based on the averaged over 2081-2100 are given for 
all of the RCP scenarios as colored vertical bars. The numbers of models used to calculate the 
multimodel means are indicated. (Figure source: adapted from Walsh et al. 2014). 


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2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

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Chapter 1 


1.5 


CD 

"O 

03 

O 

CD 

Q 


~o 

c 

CD 


03 

0 


: A 


CD 

E 

o 

c 

< 

=3 

03 

i_ 

0 

Q_ 

E 

CD 


1.0 


Surface Temperature 


Middle Tropheric 



Temperature 



NOAA 


UAH 



GISS 


STAR 

0.5 


HadCRUT4 


RSS 


0.0 

-0.5 

- 1.0 

0.6 

0.4 

0.2 

0.0 

- 0.2 

-0.4 

- 0.6 



n r 


n r 


n 1 i 1 1 1 i 1 1 1 r 


J L 


J L 


J L 


J L 




2020 


Figure 1.5. Panel A shows the annual mean temperature anomalies relative to a 1971-2000 
baseline for global mean surface temperature and global mean tropospheric temperature. A 
previous period of relatively slow warming (the “Big Hiatus”) is obvious from the mid- 1940s to 
the mid-1970s. Panel B shows the linear trend of 17-year overlapping periods (the maximum 
number of years historically for less than positive trends), plotted at the time of the center of the 
trend period. During the recent slowdown period, warming only ceased for two versions of the 
satellite data, and for a very narrow range of time periods. All 17-year trends are increasing 
rapidly as the effects of the 2015-2016 El Nino-Southern Oscillation (ENSO) event begin to 
affect the trends. Panel C shows the annual mean Pacific Decadal Oscillation (PDO) index. 
Temperature trends show a marked tendency to be lower during periods of generally negative 
PDO index, shown by the blue shading. (Figure source: adapted and updated from Trenberth 
2015 and Santer et al. 2016; Panel B, © American Meteorological Society. Used with 
pennission.) 


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2 

3 

4 

5 

6 

7 

8 

9 

10 

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Global Mean Temperature Anomalies 



Year 


Figure 1.6. Comparison of global mean temperature anomalies (°F) from observations (through 

2015) and the CMIP5 multimodel ensemble (through 2016), using the reference period 1961- 
1990. The CMIP5 multimodel ensemble (black) is constructed from blended surface temperature 
and surface air temperature data from the models, masked where observations are not available 
in the HadCRUT4 dataset (Knutson et al. 2016; see also Richardson et al. 2016). The sources for 
the three observational indices are: HadCRUT4.5 (red): 

http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html; NOAA (green): 
https://www.ncdc.noaa.gov/monitoring-references/faq/anomalies.php; and GISTEMP (blue): 
http://data.giss. nasa.gov/gistemp/tabledata_v3/GLB. Ts+dSST.txt (all downloaded on Oct. 3, 

2016) . (Figure source: adapted from Knutson et al. 2016) 


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1 


Annually-averaged Precipitation Trends 




4 Figure 1.7. Surface annually-averaged precipitation trends (change in inches) for the period 

5 1986-2015 relative to 1901-1960. The relatively coarse (0.5° x 0.5°) resolution of these maps 

6 does not capture the finer details associated with mountains, coastlines, and other small-scale 

7 effects. (Figure source: NOAA NCEI / CICS-NC). 


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Chapter 1 


1700 Years of Global Temperature Change from Proxy Data 


1.5 



Little Ice Age 

-2 I i i i i i i i i I 

300 500 700 900 1100 1300 1500 1700 1900 

Year 

1 

2 Figure 1.8. Changes in the temperature of the northern hemisphere from surface observations (in 

3 red) and from proxies (in black; uncertainty range represented by shading) relative to 1961-1990 

4 average temperature. These analyses suggest that current temperatures are higher than seen 

5 globally in at least the last 1700 years, and that the last decade (2006 to 2015) was the warmest 

6 decade on record. (Figure source: adapted from Mann et al. 2008). 

7 


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Chapter 1 


1 

2 

3 

4 

5 

6 
7 


Proxy Temperature Reconstructions 

0 200 400 600 800 1000 1200 1400 1600 1800 2000 

3.6 


0 


- 3.6 

3.6 


0 


- 3.6 

3.6 


0 


- 3.6 



3.6 


0 


- 3.6 

3.6 


0 


- 3.6 

3.6 


0 


- 3.6 

0 200 400 600 800 1000 1200 1400 1600 1800 2000 

Year C.E. 

Figure 1.9. Proxy temperatures reconstructions for the seven regions of the PAGES 2K 
Network. Temperature anomalies are relative to the 1961-1990 reference period. Grey lines 
around expected-value estimates indicate uncertainty ranges as defined by each regional group 
(see PAGE 2K et al. 2013 and related Supplementary Information). Note that the changes in 
temperature over the last century tend to occur at a much faster rate than found in the previous 
time periods. (Figure source: adapted from PAGES 2k et al. 2013) 


ill i S l lllil l! ' ' 

Arctic - multiproxy 1 


i . i.||i i i, 

- . 


'Kim 


r ' Wff 




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Chapter 1 


1 REFERENCES 

2 Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P.-P. Xie, J. Janowiak, B. Rudolf, U. 

3 Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin, 2003: The 

4 version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis 

5 (1979-present). Journal of Hydrometeorology, 4 , 1 147-1167. http://dx.doi.Org/10.l 175/1525- 

6 7541(2003)004<1 147:TVGPCP>2.0.CO;2 

7 Alexander, L.V., X. Zhang, T.C. Peterson, J. Caesar, B. Gleason, A.M.G. Klein Tank, M. 

8 Haylock, D. Collins, B. Trewin, F. Rahimzadeh, A. Tagipour, K. Rupa Kumar, J. Revadekar, 

9 G. Griffiths, L. Vincent, D.B. Stephenson, J. Burn, E. Aguilar, M. Brunet, M. Taylor, M. 

10 New, P. Zhai, M. Rusticucci, and J.L. Vazquez- Aguirre, 2006: Global observed changes in 

1 1 daily climate extremes of temperature and precipitation. Journal of Geophysical Research, 

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16 Smeets, D.V. As, R.S.W.V.d. Wal, and J. Wahr, 2015: [The Arctic] Greenland ice sheet [in 

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19 Tedesco, M., X. Fettweis, M.R.v.d. Broeke, R.S.W.v.d. Wal, C.J.P.P. Smeets, W.J.v.d. Berg, 

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23 Tedesco, M., X. Fettweis, T. Mote, J. Wahr, P. Alexander, J.E. Box, and B. Wouters, 2013: 

24 Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional 

25 climate model and reanalysis data. The Cryosphere, 7 , 615-630. http://dx.doi.org/10.5194/tc- 

26 7-615-2013 

27 Thornton, P.E., S.C. Doney, K. Lindsay, J.K. Moore, N. Mahowald, J.T. Randerson, I. Fung, J.F. 

28 Lamarque, J.J. Feddema, and Y.H. Lee, 2009: Carbon-nitrogen interactions regulate climate- 

29 carbon cycle feedbacks: results from an atmosphere-ocean general circulation model. 

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32 http://dx.doi.org/10. 1 126/science. aac9225 


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2 Sheffield, 2014: Global warming and changes in drought. Nature Climate Change, 4 , 17-22. 

3 http://dx.doi.org/10.1038/nclimate2067 

4 Turney, C.S.M. and R.T. Jones, 2010: Does the Agulhas Current amplify global temperatures 

5 during super-interglacials? Journal of Quaternary Science, 25, 839-843. 

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7 van der Werf, G.R., J.T. Randerson, L. Giglio, G.J. Collatz, M. Mu, P.S. Kasibhatla, D.C. 

8 Morton, R.S. DeFries, Y. Jin, and T.T. van Leeuwen, 2010: Global fire emissions and the 

9 contribution of deforestation, savanna, forest, agricultural, and peat fires (1997-2009). 

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11 11707-2010 

12 Velicogna, I. and J. Wahr, 2013: Time -variable gravity observations of ice sheet mass balance: 

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15 Vose, R.S., D. Amdt, V.F. Banzon, D.R. Easterling, B. Gleason, B. Huang, E. Kearns, J.H. 

16 Lawrimore, M.J. Menne, T.C. Peterson, R.W. Reynolds, T.M. Smith, C.N. Williams, and 

17 D.L. Wuertz, 2012: NOAA’s Merged Land-Ocean Surface Temperature Analysis. Bulletin of 

18 the American Meteorological Society, 93 , 1677-1685. http://dx.doi.org/10.1175/BAMS-D- 

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20 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

21 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

22 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

23 Climate Change Impacts in the United States: The Third National Climate Assessment. 

24 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

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26 Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C.R. Mechoso, 2014: A global perspective on 

27 CMIP5 climate model biases. Nature Climate Change, 4 , 201-205. 

28 http://dx.doi.org/10.1038/nclimate21 18 

29 Willett, K.M., D.J. Philip, W.T. Peter, and P.G. Nathan, 2010: A comparison of large scale 

30 changes in surface humidity over land in observations and CMIP3 general circulation 

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33 Williams, S.D.P., P. Moore, M.A. King, and P.L. Whitehouse, 2014: Revisiting GRACE 

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1 Zaehle, S., P. Friedlingstein, and A.D. Friend, 2010: Terrestrial nitrogen feedbacks may 

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9 Caceres, G. Casassa, G. Cobos, L.R. Davila, H. Delgado Granados, M.N. Demuth, L. 

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1 1 Z. Li, M. Pelto, P. Pitte, V.V. Popovnin, C.A. Portocarrero, R. Prinz, C.V. Sangewar, I. 

12 Severskiy, O. SigurSsson, A. Soruco, R. Usubaliev, and C. Vincent, 2015: Historically 

13 unprecedented global glacier decline in the early 21st century. Journal of Glaciology, 61 , 

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15 Zhang, R. and T.R. Knutson, 2013: The role of global climate change in the extreme low 

16 summer Arctic sea ice extent in 2012 [in "Explaining Extremes of 2012 from a Climate 

17 Perspective"]. Bulletin of the American Meteorological Society, 94 , S23-S26. 

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19 Zhang, X., H. Wan, F.W. Zwiers, G.C. Hegerl, and S.-K. Min, 2013: Attributing intensification 

20 of precipitation extremes to human influence. Geophysical Research Letters , 40 , 5252-5257. 

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22 Zhang, X., F.W. Zwiers, G.C. Hegerl, F.H. Lambert, N.P. Gillett, S. Solomon, P.A. Stott, and T. 

23 Nozawa, 2007: Detection of human influence on twentieth-century precipitation trends. 

24 Nature, 448 , 461-465. http://dx.doi.org/10.1038/nature06025 

25 Zhu, Z., S. Piao, R.B. Myneni, M. Huang, Z. Zeng, J.G. Canadell, P. Ciais, S. Sitch, P. 

26 Friedlingstein, A. Arneth, C. Cao, L. Cheng, E. Kato, C. Koven, Y. Li, X. Lian, Y. Liu, R. 

27 Liu, J. Mao, Y. Pan, S. Peng, J. Penuelas, B. Poulter, T.A.M. Pugh, B.D. Stocker, N. Viovy, 

28 X. Wang, Y. Wang, Z. Xiao, H. Yang, S. Zaehle, and N. Zeng, 2016: Greening of the Earth 

29 and its drivers. Nature Climate Change, 6, 791-795. http://dx.doi.org/10.1038/nclimate3004 

30 Zunz, V., H. Goosse, and F. Massonnet, 2013: How does internal variability influence the ability 

31 of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? The 

32 Ctyosphere, 7 , 451-468. http://dx.doi.org/10.5194/tc-7-451-2013 


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1 2. Physical Drivers of Climate Change 

2 Key Findings 

3 1. Human activities continue to significantly affect Earth’s climate by altering factors that 

4 change its radiative balance (known as a radiative forcing). These factors include greenhouse 

5 gases, small airborne particles (aerosols), and the reflectivity of the Earth’s surface. In the 

6 industrial era, human activities have been and remain the dominant cause of climate wanning 

7 and have far exceeded the relatively small net increase due to natural factors, which include 

8 changes in energy from the sun and the cooling effect of volcanic eruptions. ( Very high 

9 confidence) 

10 2. Aerosols caused by human activity play a profound and complex role in the climate system 

1 1 through direct radiative effects and indirect effects on cloud formation and properties. The 

12 combined forcing of aerosol-radiation and aerosol-cloud interactions is negative over the 

13 industrial era, substantially offsetting a substantial part of greenhouse gas forcing, which is 

14 currently the predominant human contribution ( high confidence) . The magnitude of this 

15 offset has declined in recent decades due to a decreasing trend in net aerosol forcing. 

16 ( Medium to high confidence) 

17 3. The climate system includes a number of positive and negative feedback processes that can 

18 either strengthen (positive feedback) or weaken (negative feedback) the system’s responses 

19 to human and natural influences. These feedbacks operate on a range of timescales from very 

20 short (essentially instantaneous) to very long (centuries). While there are large uncertainties 

21 associated with some of these feedbacks, the net feedback effect over the industrial era has 

22 been positive (amplifying warming) and will continue to be positive in coming decades. 

23 ( High confidence ) 

24 

25 2.1 Earth’s Energy Balance and the Greenhouse Effect 

26 The temperature of the Earth system is determined by the amounts of incoming (short- 

27 wavelength) and outgoing (both short- and long-wavelength) radiation. In the modem era, the 

28 magnitudes of these flows are accurately determined from satellite measurements. Figure 2. 1 

29 shows that about a third of incoming, short-wavelength energy from the sun is reflected back to 

30 space and the remainder absorbed by the Earth system. The fraction of sunlight scattered back to 

3 1 space is determined by the reflectivity (albedo) of land surfaces (including snow and ice), 

32 oceans, and clouds and particles in the atmosphere. The amount and albedo of clouds, snow 

33 cover, and ice cover are particularly strong determinants of the amount of sunlight reflected back 

34 to space because their albedos are much higher than that of land and oceans. 


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In addition to reflected sunlight, the Earth loses energy through infrared (long-wavelength) 
radiation from the surface and atmosphere. Greenhouse gases in the atmosphere absorb some of 
this radiation, much of which is re-radiated back towards the surface (Figure 2.1) where it is 
absorbed, further heating the Earth; the remainder is emitted to space. The naturally occurring 
greenhouse gases in Earth’s atmosphere— principally water vapor and carbon dioxide— keep the 
near-surface air temperature about 33°C (60°F) warmer than it would be in their absence. 
Geothermal heat from the Earth’s interior, direct heating from energy production, and frictional 
heating through tidal flows also contribute to the amount of energy available for heating the 
Earth’s surface and atmosphere, but their contribution is an extremely small fraction (<0.1%) of 
that due to net solar (shortwave) and infrared (longwave) radiation, (e.g., see Davies and Davies 
2010; Flanner 2009; Munk and Wunsch 1998 for estimates of these forcings). 

[INSERT FIGURE 2.1 HERE: 

Figure 2.1: Global mean energy budget of the Earth under present-day climate conditions. 
Numbers state magnitudes of the individual energy fluxes in watts per square meter (W/m 2 ) 
averaged over Earth’s surface, adjusted within their uncertainty ranges to balance the energy 
budgets of the atmosphere and the surface. Numbers in parentheses attached to the energy fluxes 
cover the range of values in line with observational constraints. These constraints are largely 
provided by satellite-based observations, which have directly measured solar and infrared fluxes 
at the top of the atmosphere over nearly the whole globe since 1984 (Barkstrom 1984; Smith et 
al. 1994). More advanced satellite-based measurements focusing on the role of clouds in Earth’s 
radiative fluxes, have been available since 1998 (Wielicki et al. 1995, 1996). (Figure source: 
IPCC 2013; © IPCC, used with permission).] 

Thus, Earth’s equilibrium temperature is controlled by a short list of factors: incoming sunlight, 
absorbed and reflected sunlight, emitted infrared radiation, and infrared radiation absorbed in the 
atmosphere, primarily by greenhouse gases. Changes in these factors affect Earth’s radiative 
balance and therefore its climate, including but not limited to the average, near-surface air 
temperature. Anthropogenic activities have changed the Earth’s radiative balance and its albedo 
by adding greenhouse gases, particles (aerosols), and aircraft contrails to the atmosphere, and 
through land-use changes. 

Changes in the radiative balance produce changes in temperature, precipitation, and other climate 
variables through a complex set of physical processes, many of which are coupled (Figure 2.2). 

In the following sections, the principal components of the framework shown in Figure 2.2 are 
described. Climate models are structured to represent these processes; climate models, and their 
components and associated uncertainties, are discussed in more detail in Chapter 4: Projections. 

[INSERT FIGURE 2.2 HERE: 

Figure 2.2 Simplified conceptual modeling framework for the climate system as implemented in 
many climate models (Chapter 4). Modeling components include forcing agents, feedback 
processes, carbon uptake processes and radiative forcing and balance. The lines indicate physical 


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interconnections (solid lines) and feedback pathways (dashed lines). Principal changes (blue 
boxes) lead to climate impacts (red box) and feedbacks. (Figure source: adapted from Knutti and 
Rugenstein 2015).] 

The processes and feedbacks connecting changes in Earth’s radiative balance to a climate 
response (Figure 2.2) operate on a large range of timescales. Reaching an equilibrium 
temperature distribution in response to anthropogenic activities takes decades or longer because 
the Earth system — in particular the oceans and cryosphere — are slow to respond due to their 
large thermal masses and the long timescale of circulation between the ocean surface and the 
deep ocean. Of the substantial energy gained in the combined ocean-atmosphere system over the 
previous four decades, over 90% of it has gone into ocean wanning (Rhein et al. 2014; see Box 

3.1 Fig 1). Even at equilibrium, internal variability in the Earth’s climate system causes limited 
annual to decadal-scale variations in regional temperatures and other climate parameters that do 
not contribute to long-term trends. For example, it is likely that natural variability has led to 
between -0.1 °C (-0.1 8°F) and 0.1 °C (0.1 8°F) changes in surface temperatures from 1951 to 
2010; by comparison, anthropogenic greenhouse gases have likely contributed between 0.5°C 
(0.9°F) and 1.3°C (2.3°F) to observed surface warming over this same period (Bindoff et al. 
2013). Due to these longer timescale responses and natural variability, changes in Earth’s 
radiative balance are not realized immediately as changes in climate, and even in equilibrium 
there will always be variability around mean trends. 

2.2 Radiative Forcing (RF) and Effective Radiative Forcing (ERF) 

Radiative forcing (RF) is widely used to quantify a radiative imbalance in Earth’s atmosphere 
resulting from either natural changes or anthropogenic activities. It is expressed as a change in 
net radiative flux (W/m 2 ) at the tropopause or top of the atmosphere over the industrial era 
(Myhre et al. 2013). RF serves as a metric to compare present, past, or future perturbations to the 
climate system (e.g. Boer and Yu 2003; Gillett et al. 2004; Matthews et al. 2004; Meehl et al. 
2004; Jones et al. 2007; Mahajan et al. 2013; Shiogama et al. 2013). The equilibrium surface 
temperature response (AT) to a forcing (RF) is given by AT = X RF where X is the climate 
sensitivity factor (Knutti and Hegerl 2008; Flato et al. 2013). For clarity and consistency, RF 
calculations require that a time period be defined over which the forcing occurs. Here, this period 
is the industrial era, defined as beginning in 1750 and extending to 201 1, unless otherwise noted. 
The 2011 end date is that adopted by the CMIP5 calculations, which are the basis of RF 
evaluations by the Intergovernmental Panel on Climate Change (IPCC; Myhre et al. 2013). In 
practice, the calculation of RF over a given period is defined in several ways based on where it is 
evaluated (tropopause or top of the atmosphere) and on assumptions concerning, for example, 
whether the surface or stratospheric temperature is allowed to respond (Myhre et al. 2013). In 
this report, we follow the IPCC recommendation that the RF caused by a forcing agent be 
evaluated as the net radiative flux change at the tropopause after stratospheric temperatures have 
adjusted to a new equilibrium while assuming all other variables (for example, temperatures and 


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1 cloud cover) are held fixed (Myhre et al. 2013). A change that results in a net increase in the 

2 downward flux at the tropopause constitutes a positive RF, normally resulting in a warming of 

3 the surface and/or atmosphere, and potentially changes in other climate parameters. Conversely, 

4 a change that yields an increase in the net upward flux constitutes a negative RF, leading to a 

5 cooling of the surface and/or atmosphere, and potentially changes in other climate parameters. 

6 A refinement of the RF concept introduced in the latest IPCC assessment (IPCC 2013) is the use 

7 of effective radiative forcing (ERF). ERF for a climate driver is defined is its RF plus all rapid 

8 adjustment s) to that RF (Myhre et al. 2013). These rapid adjustments occur on timescales much 

9 shorter than, for example, the response of ocean temperatures. For an important subset of climate 

10 drivers, ERF is more reliably correlated with the climate response to the forcing than is RF; as 

1 1 such, it is an increasingly used metric when discussing forcing. For atmospheric components, 

12 ERF includes rapid adjustments due to direct warming of the troposphere, which produces 

13 horizontal temperature variations, variations in the vertical lapse rate, and changes in clouds and 

14 vegetation, and it includes the microphysical effects of aerosols on cloud lifetime. Not included 

15 in ERF are climate responses driven by surface air temperature changes. For aerosols in surface 

16 snow, ERF includes the effects of direct warming of the snowpack by particulate absorption (for 

17 example, snow-grain size changes). The largest differences between RF and ERF occur for 

18 forcing by light-absorbing aerosols because of their influence on clouds and snow. Changes in 

19 these climate parameters can be quantified in terms of their impact on radiative fluxes (for 

20 example, albedo). For example, black carbon (BC) aerosol in the atmosphere absorbs sunlight, 

21 producing a positive RF. In addition, this absorption warms the atmosphere; on net this response 

22 is expected to increase cloud cover and therefore increase planetary albedo (the “semi-direct 

23 effect”). This “rapid response” lowers the ERF of atmospheric BC by approximately 15% 

24 relative to its RF from direct absorption alone (Bond et al. 2013). For BC deposited on snow, the 

25 ERF is a factor of three higher than the RF because of the positive feedbacks of reducing snow 

26 albedo and increasing snow melt (e.g., Flanner et al. 2009; Bond et al. 2013). For most non- 

27 aerosol climate drivers the differences are small. 

28 2.3 Drivers of Climate Change over the Industrial Era 

29 Climate drivers of significance over the industrial era include both those associated with 

30 anthropogenic activity and those of natural origin. The only significant natural climate drivers in 

3 1 the industrial era are changes in solar irradiance and volcanic eruptions. Natural emissions and 

32 sinks of greenhouse gases and aerosols have varied over the industrial era but have not 

33 contributed significantly to RF. Other known drivers of natural origin that operate on longer 

34 timescales are changes in Earth’s orbit (that is, the Milankovich cycles), asteroids, changes in 

35 atmospheric CO 2 via chemical weathering of rock, and potentially cosmic rays. Anthropogenic 

36 drivers can be divided into a number of categories, including well-mixed greenhouse gases 

37 (WMGHGs), short-lived climate forcers (SLCFs, which include methane, some 

38 hydrofluorocarbons [HFCs], ozone, and aerosols), contrails, and changes in albedo (for example, 


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land-use changes). Some WMGHGs are also considered SLCFs (for example, methane). Figure 
2.3 summarizes RF and/or ERF for the principal climate drivers in the industrial era. Each is 
described briefly in the following. 


[INSERT FIGURE 2.3 HERE: 


Figure 2.3 Bar chart for RF (hatched) and ERF (solid) for the period 1750-2011, where the total 

ERF is derived from IPCC. Uncertainties (5% to 95% confidence range) are given for RF (dotted 


lines) and ERF (solid lines). Volcanic forcing is not shown because this forcing is negligible over 

the industrial era. (Figure source: IPCC 2013© IPCC, used with permission).] 


2.3.1 Natural Drivers 


SOLAR IRRADIANCE 


Solar irradiance changes directly impact the climate system because the irradiance is its primary 
source of energy (Lean 1997). At the century scale, the largest variations in total solar irradiance 
(TSI) are associated with the 1 1-year solar cycle (Frolich and Lean 2004; Gray et al. 2010), 
direct observations of which have been available since 1978 (Kopp 2014) though proxy 
indicators of solar cycles are available back to the early 1600s (Kopp et al. 2016). Although the 
variations in TSI amount to only 0.1% of the sun’s total output of about 1360 W/m 2 (Kopp and 
Lean 2011), variations in irradiance at specific wavelengths can be much larger (tens of percent). 
Solar spectral irradiance (SSI) is most variable at near-ultraviolet (UV) and shorter wavelengths 
(Floyd et al. 2003), which are also the most important in driving changes in ozone (Ermolli et al. 
2013; Bolduc et al. 2015). Variations in TSI and SSI can thus induce important changes in the 
stratosphere and troposphere, both through direct heating and through changes in stratospheric 
ozone that in turn further affect heating rates and both stratospheric and tropospheric circulation 
(Gray et al. 2010; Lockwood 2012, Seppala et al. 2014). Further, the relationships between 
changes in TSI and changes in atmospheric composition, heating, and dynamics are complex. 
Changes in UV irradiance can be out of phase with changes in TSI with mixed consequences, for 
example, for the net production and destruction of stratospheric ozone (Ball et al. 2016). As a 
result, changes in TSI are not directly correlated with the resulting radiative flux changes 
(Ermolli et al. 2013; Xu and Powell 2013; Gao et al. 2015). 

IPCC has provided an estimate of the TSI RF of 0.05 W/m 2 (range: 0.0 to 0.10 W/m 2 ) (Myhre et 
al. 2013). This forcing does not account for radiative flux changes resulting from SSI-driven 
changes in stratospheric ozone. Understanding of the link between changes in SSI, stratospheric 
ozone, heating rates, and circulation changes has recently improved using, in particular, satellite 
data starting in 2002 that provide SSI measurements through the UV (Ermolli et al. 2013) along 
with a series of chemistry-climate modeling studies (Swartz et al. 2012; Chiodo et al. 2014; 
Dhomse et al. 2013; Ermolli et al. 2013; Bolduc et al. 2015). At the regional scale, circulation 
changes driven by SSI variations may be significant for some locations and seasons, but this is 
not yet sufficiently understood to quantify (Lockwood 2012). Despite remaining uncertainties, 


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1 there is very high confidence that solar radiance-induced changes in RF are small relative to RF 

2 from anthropogenic greenhouse gases over the industrial era (Myhre et al. 2013) (Figure 2.3). On 

3 millennial timescales, changes in solar output are expected to have influenced climate but there 

4 is uncertainty in extending the TSI and SSI records back in time. 

5 VOLCANOES 

6 Explosive volcanic eruptions inject sulfur dioxide (SO 2 ) and ash into the stratosphere, which 

7 leads to significant short-term climate effects (Myhre et al. 2013, and references therein). SO 2 

8 oxidizes to fonn sulfuric acid (H2SO4) which condenses, forming new particles or adding mass 

9 to preexisting particles, thereby substantially enhancing the attenuation of sunlight transmitted 

10 through the stratosphere (that is, increasing the aerosol optical depth). These aerosols increase 

1 1 the Earth’s albedo by scattering sunlight back to space, creating a negative RF that cools the 

12 planet (Andronova et al. 1999; Robock 2000). The RF persists for the lifetime of aerosol in the 

13 stratosphere, which is a few years, far exceeding that in the troposphere (about a week). Volcanic 

14 RF is integrated by the ocean, resulting in ocean cooling and associated changes in ocean 

15 circulation patterns that last for decades after major eruptions (for example, Mt. Tambora in 

16 1815) (Stenchikov et al. 2009; Ottera et al. 2010; Zanchettin et al. 2012; Zhang et al. 2013). In 

17 addition to the direct RF, volcanic aerosol heats the stratosphere, altering circulation patterns and 

1 8 destroying ozone, which further changes heating and circulation. The resulting impacts on 

19 advective heat transport can be larger than the temperature impacts of the direct forcing (Robock 

20 2000). Aerosol from both explosive and non-explosive eruptions also affects the troposphere 

21 through changes in diffuse radiation and through aerosol-cloud interactions, both through the 

22 initial emissions and later when volcanic aerosol eventually sediments out of the stratosphere. It 

23 has been proposed that major eruptions might “fertilize” the ocean with sufficient iron to affect 

24 phyotoplankton production and therefore the ocean CO 2 sink, though this is a new area of 

25 research (Langmann 2014). Volcanoes also emit CO 2 and water vapor, although in small 

26 quantities relative to other emissions. Annual CO 2 emissions from volcanoes are conservatively 

27 estimated at <1% that from anthropogenic activities (Gerlach 2011). The magnitude of volcanic 

28 effects on climate depend on the number and strengths of eruptions, the latitude of injection and, 

29 for ocean temperature and circulation impacts, the timing of the eruption relative to ocean 

30 temperature and circulation patterns (Zanchettin et al. 2012; Zhang et al. 2013). 

3 1 Volcanic eruptions are the largest natural forcings within the industrial era and in the last 

32 millennium caused multiyear transient episodes of negative RF of up to several W/m 2 (Figure 

33 2.5). The RF of the last major volcanic eruption, Mt. Pinatubo in 1991, decayed to negligible 

34 values later in the 1990s, with the temperature signal lasting about twice as long due to the 

35 effects of changes in ocean heat uptake (Stenchikov et al. 2009). The present day volcanic RF 

36 evaluated for periods since 2000 yields values of about -0.1 W/m 2 or less due to several small 

37 non-explosive eruptions. A net volcanic RF has been omitted from the drivers of climate change 

38 in the industrial era in Figure 2.3 because the episodic short-term nature of volcanic RF is not 

39 comparable with the other climate drivers, which produce long-tenn effects. While future 


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explosive volcanic eruptions have the potential to again alter Earth’s climate for periods of 
several years, predictions of occurrence, intensity, and location remain elusive. 

2.3.2 Anthropogenic Drivers 

PRINCIPAL WELL-MIXED GREENHOUSE GASES (WMGHGs) 

The principal WMGHGs are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). 
These gases have modest-to-small regional variabilities and, with atmospheric lifetimes of a 
decade or more, are circulated and mixed around the globe to yield small inter-hemispheric 
gradients. The atmospheric abundances and associated radiative forcings of WMGHGs have 
increased substantially over the industrial era (Figures 2. 4-2. 6). Contributions from natural 
sources of these constituents are accounted for in these industrial-era RF calculations. 

[INSERT FIGURE 2.4 HERE: 

Figure 2.4 Atmospheric concentrations of carbon dioxide, methane, and nitrous oxide over the 
last 10,000 years (large panels) and since 1750 (inset panels). Measurements are shown from ice 
cores (symbols with different colors for different studies) and atmospheric samples (red lines). 
The corresponding radiative forcings are shown on the right-hand axes of the large panels. The 
concentrations of these gases have continued to increase in the 2000 to 2016 period 
( http ://www.esrl.noaa. gov/ gmd/ccgg/ aggi .html) . (Figure source: IPCC 2007© IPCC, used with 
permission)] % 

[INSERT FIGURE 2.5 HERE: 

Figure 2.5 (a) Radiative forcing (RF) from the major WMGHGs and groups of halocarbons 
(Others) from 1850 to 201 1; (b) the data in (a) with a logarithmic scale; (c) RFs from the minor 
WMGHGs from 1850 to 201 1 (logarithmic scale); (d) rate of change in forcing from the major 
WMGHGs and halocarbons from 1850 to 201 1. (Figure source: IPCC 2013; © IPCC, used with 
permission).] 

[INSERT FIGURE 2.6 HERE: 

Figure 2.6 Effective radiative forcing changes across the industrial era for anthropogenic and 
natural forcing mechanisms. Also shown are the sum of all forcings (Total) and the sum of 
anthropogenic forcings (Total Anthropogenic). Bars with the forcing and uncertainty ranges (5% 
to 95% confidence range) at present are given in the right part of the figure. For aerosol, the ERF 
due to aerosol-radiation interaction and total aerosol ERF are shown. The uncertainty ranges are 

for present (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty in 

the total aerosol ERF is given. For several of the forcing agents the relative uncertainty may be 

larger for certain time periods compared to present. See IPCC AR5 Supplementary Material 
Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers provided 

in Annex II of this report. The total anthropogenic forcing was 0.57 (0.29 to 0.85) W/m 2 in 1950, 

1.25 (0.64 to 1.86) W/m 2 in 1980 and 2.29 (1.13 to 3.33) W/m 2 in 2011. (Figure source: IPCC 

2013; © IPCC, used with permission).] 


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CO 2 has substantial global sources and si nk s (Figure 2.7). CO 2 emission sources have grown in 
the industrial era primarily from fossil fuel combustion (that is, coal, gas, oil), cement 
manufacturing, and land-use change from activities such as deforestation Ciais et al. 2013). 
Processes that remove emitted CO 2 from the atmosphere include uptake in the oceans, residual 
land uptake, and ultimately rock weathering, thereby yielding an atmospheric lifetime of many 
decades to millennia, far greater than any other major GHG. Seasonal variations in CO 2 
atmospheric concentrations occur in response to transpiration in the biosphere, and to a lesser 
degree due to seasonal variations in anthropogenic emissions. In addition to fossil fuel reserves, 
there are large natural reservoirs of carbon in the oceans, in vegetation and soils, and in 
permafrost. In the industrial era, the CO 2 atmospheric growth rate has been exponential (Figure 
2.4), with the increase in atmospheric CO 2 approximately twice the ocean uptake. Over the last 
50 years or more, CO 2 has shown the largest annual concentration and RF increases among all 
GHGs (Figures 2.4 and 2.5). The global average CO 2 concentration has increased by 40% over 
the industrial era, increasing from 278 parts per million (ppm) in 1750 to 390 ppm in 201 1 (Ciais 
et al. 2013); it now exceeds 400 ppm (2016) (http://www.esrl.noaa.gov/gmd/ccgg/trends/). CO 2 
has been chosen as the reference in defining the global warming potential (GWP) of other GFIGs 
and climate agents. The GWP of a GHG is the integrated RF over a specified time period (for 
example, 100 years) from the emission of a given mass of the GHG divided by the integrated RF 
from the same mass emission of CO 2 . 

[INSERT FIGURE 2.7 HERE: \ 

Figure 2.7 | CO 2 sources and sinks (PgC/yr 1 ) over the industrial era (1750-201 1). The 
partitioning of atmospheric emissions among the atmosphere, land, and ocean is shown as 
equivalent negative emissions in the lower panel; of these, the land and ocean terms are true 
si nk s of atmospheric CO 2 . The top panel shows an expanded view of emissions from fossil fuels 
and cement manufacturing. The atmospheric CO 2 growth rate is derived from atmospheric 
observations and ice core data. The ocean CO 2 sink is derived from a combination of models and 
observations. The land sink is the residual of the other terms in a balanced CO 2 budget, and 
represents the sink of anthropogenic CO 2 in natural land ecosystems. These terms only represent 
changes since 1750 and do not include natural CO 2 fluxes (for example, from weathering and 
outgassing from lakes and rivers). (Figure source: Ciais et al. 2014; © IPCC, used with 
permission)] 

Methane concentrations and RF have also grown substantially in the industrial era (Figures 2.4 
and 2.5). Methane is a much more potent greenhouse gas than CO 2 but it has a shorter 
atmospheric lifetime of about 12 years. Methane also has indirect climate effects through 
induced changes in CO 2 , stratospheric water vapor and ozone (Lelieveld and Crutzen 1992). The 
100-year GWP of methane is high (28, direct; 34 including indirect), and its 20-year GWP is 
even higher (84; 86) (Myhre et al. 2013 Table 8.7). With a current global value near 1840 parts 
per billion by volume (ppb), methane concentrations have increased by a factor of about 2.5 over 


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1 the industrial era. Variability in the methane annual growth rate over the past several decades has 

2 been larger than for CO 2 and N 2 O, and occasionally negative for short periods. 

3 Methane has a variety of natural and anthropogenic sources estimated to total 556±56Tg CH 4 

4 per year in 201 1, with the anthropogenic fraction estimated to be about 60% (Ciais et al. 2013). 

5 The methane budget is complicated by the variety of natural and anthropogenic sources and si nk s 

6 that influence its atmospheric concentration. These include the global abundance of the hydroxyl 

7 radical (OH), which controls the methane atmospheric lifetime; changes in large-scale 

8 anthropogenic activities such as mining, natural gas extraction, animal husbandry, and 

9 agricultural practices; and natural wetland emissions. The remaining uncertainty in the cause(s) 

10 of the approximately 20 -year negative trend in the methane annual growth rate starting in the 

1 1 mid-1980s reflects the budget complexity (IPCC 2013). 

12 Growth in nitrous oxide concentrations and RF over the industrial era are smaller than for CO 2 

13 and methane (Figures 2.4 and 2.5). Nitrous oxide is emitted in the nitrogen cycle in natural 

14 ecosystems and has a variety of anthropogenic sources, including the use of synthetic fertilizers 

15 in agriculture, motor vehicle exhaust, and some manufacturing processes. The current global 

16 value near 330 ppb reflects steady growth over the industrial era with average increases in recent 

17 decades of 0.75 ppb per year (Ciais et al. 2013) (Figure 2.4). Fertilization in global food 

18 production is estimated to be responsible for 80% of the growth rate. Anthropogenic sources 

19 account for approximately 40% of the annual N20 emissions of 17.9 ( 8 . 1 to 30.7) TgN. Nitrous 

20 oxide has an atmospheric lifetime of about 120 years and GWP of 265 (direct; Myhre et al. 2013 

21 Table 8.7). The primary sink of nitrous oxide is photochemical destruction in the stratosphere, 

22 which produces nitrogen oxides (NOx) that catalytically destroy ozone (e.g. Skiba and Rees 

23 2014). Small indirect climate effects, such as the response of stratospheric ozone, are generally 

24 not included in the nitrous oxide RF. 

25 Nitrous oxide is a component of the larger global budget of total N comprising N 2 O, ammonia 

26 (NH3), and reactive nitrogen (NOx). Significant uncertainties are associated with balancing this 

27 budget over oceans and land while accounting for deposition and emission processes (Ciais et al. 

28 2013). Furthennore, changes in climate parameters such as temperature, moisture, and CO 2 

29 concentrations are expected to affect the N 2 O budget in the future, and perhaps atmospheric 

30 concentrations. 

3 1 OTHER WELL-MIXED GREENHOUSE GASES 

32 Other WMGHGs primarily include several categories of synthetic gases, including 

33 chlorofluorocarbons (CFCs), halons, hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons 

34 (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF 6 ). These gases entered the 

35 atmosphere as early as the mid-20th century, beginning with the expanded use of CFCs as 

36 refrigerants and in other applications. The rapid growth of CFCs declined beginning in the 1990s 

37 with their regulation as ozone-depleting substances under the United Nations Montreal Protocol 


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1 (Figure 2.4). All of these gases are greenhouse gases covering a wide range of GWPs, 

2 atmospheric concentrations, and trends. PFCs, SF6, and HFCs are in the basket of gases covered 

3 under the United Nations Framework Convention on Climate Change. The United States has 

4 joined other countries in proposing that HFCs be controlled as a WMGHG under the Montreal 

5 Protocol because of their large projected future abundances (http://in.state.gov/mc7062 1 .htm). In 

6 October 2016, the Montreal Protocol adopted an amendment to phase down global HFC 

7 production and consumption, avoiding emissions of an estimated 105 Gt C02- eq by 2100 

8 (http://ozone.unep.org/sites/ozone/files/pdfs/FAQs_Kigali-Amendment.pdf). CFCs, HCFCs, 

9 HFCs, halons and a few other gases comprise atmospheric halocarbons. The atmospheric growth 

10 rates of some halocarbon concentrations are large (for example, SF6 and HFC- 134a), although 

1 1 their RF contributions remain small (Figure 2.4). 

12 WATER VAPOR 

13 Water vapor in the atmosphere acts as a powerful natural GHG, significantly increasing the 

14 Earth’s equilibrium temperature. In the stratosphere, water vapor abundances are controlled by 

15 transport from the troposphere and from oxidation of methane. Increases in methane from 

16 anthropogenic activities therefore increase stratospheric water vapor, producing a positive RF 

17 (e.g. Solomon et al. 2010; Hegglin et al. 2014). Other less-important anthropogenic sources of 

18 stratospheric water vapor are hydrogen oxidation (le Texier et al. 1988), aircraft exhaust 

19 (Rosenlof et al. 2001; Morris et al. 2003), and explosive volcanic eruptions (Loffler et al. 2016). 

20 In the troposphere, changes in troposphere water vapor are considered a feedback in the climate 

21 system (see 2.6. 1 and Figure 2.2). As GHGs warm the atmosphere, tropospheric water vapor 

22 concentrations increase, thereby amplifying the warming effect (Held and Soden 2000). 

23 OZONE 

24 Ozone is a naturally occurring GHG. Ozone changes in the troposphere and stratosphere in 

25 response to anthropogenic and natural emissions. The changes generally have substantial spatial 

26 and temporal variability due to the nature of the production, loss, and transport processes 

27 controlling ozone abundances. Ozone RF calculations are complex because ozone naturally 

28 occurs in both the troposphere and stratosphere and has a lifetime that varies by atmospheric 

29 region. In the global troposphere, photochemical ozone fonnation is increased by emissions of 

30 methane, NO x , carbon monoxide (CO), and non-methane volatile organic compounds (VOCs), 

3 1 yielding a positive RF near and downwind of these precursor source emissions (e.g., Dentener et 

32 al. 2005). Stratospheric ozone is photochemically destroyed in reactions involving halogen 

33 species chlorine and bromine. Halogens are released in the stratosphere from the decomposition 

34 of synthetic halocarbons emitted at the surface (WMO 2014). Stratospheric ozone depletion, 

35 which is most notable in the polar regions, yields a net negative RF (Myhre et al. 2013). 

36 


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1 AEROSOLS 

2 Atmospheric aerosols are perhaps the most complex and are the most uncertain component of 

3 forcing due to anthropogenic activities (Myhre et al. 2013). Aerosols have diverse natural and 

4 anthropogenic sources, and emissions from these sources can interact in non-linear ways 

5 (Boucher et al. 2013). Aerosol types are categorized by composition; namely, sulfate, black 

6 carbon, organic aerosols, nitrate, dust, and sea salt. Individual particles generally include a mix 

7 of these components due to both chemical and physical transformations of aerosols and aerosol 

8 precursor gases following emission. Aerosol tropospheric lifetimes are days to weeks due to the 

9 general hydroscopic nature of primary and secondary particles and the ubiquity of cloud and 

10 precipitation systems in the troposphere. Particles which act as cloud condensation nuclei (CCN) 

11 or which are scavenged by cloud droplets are removed from the troposphere in precipitation. The 

12 heterogeneity of aerosol sources and locations combined with short aerosol lifetimes leads to the 

13 high spatial and temporal variabilities observed in global aerosol distributions and forcings. 

14 Aerosols from anthropogenic activities influence RF in three primary ways: through the aerosol- 

15 radiation interaction (RFari), the aerosol-cloud interaction (RFaci), and the albedo change from 

16 absorbing-aerosol deposition on snow and ice (Boucher et al. 2013). RFari is also known as the 

17 aerosol “direct effect,” involving absorption and scattering of longwave and shortwave radiation. 

18 RFaci is also known as the cloud albedo “indirect effect” from changes in cloud particle number. 

19 Global net RFari and RFaci are negative (Myhre et al. 2013), although light-absorbing aerosol 

20 components (for example, BC) have positive RF (Bond et al. 2013). The complexity of aerosol 

21 forcing increases with the use of ERF. EFRaci incorporates the rapid adjustment from the semi- 

22 direct effect of absorbing aerosol (that is, the cloud response to atmospheric heating) and 

23 includes cloud lifetime effects (for example, glaciation and thermodynamic effects) (Boucher et 

24 al. 2013). Light-absorbing aerosols also affect climate when present in surface snow, by lowering 

25 surface albedo (e.g. Flanner et al. 2009). There is very high confidence that the RF from snow 

26 and ice albedo is positive; as noted above, the ERF of this forcing is significantly higher than its 

27 RF (Bond et al. 2013). Aerosol RF and ERF calculations and uncertainties continue to improve, 

28 as noted by IPCC (Boucher et al. 2013), as aerosol observations become more available and 

29 aerosol model skill improves. 

30 LAND SURFACE 

3 1 Land-cover changes (LCC) due to anthropogenic activities in the industrial era have changed the 

32 land surface brightness. There is strong evidence that these changes have increased Earth’s 

33 surface albedo, creating a globally averaged net-negative RF (Myhre et al. 2013). In specific 

34 regions, however, LCC has produced a positive RF by lowering surface albedo (for example 

35 through afforestation and pasture abandonment). In addition to the direct radiative forcing 

36 through albedo changes, land-cover changes also have indirect effects on climate, such as 

37 altering the hydrologic and carbon cycles and altering dust emissions. These effects are generally 

38 not included in the LCC RF calculations, and the sign of their forcing may be opposite that of the 


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1 LCC albedo forcing. Some of these responses, such as alteration of the carbon cycle, constitute 

2 climate feedbacks (Figure 2.2), as discussed more extensively in Chapter 10 (Changes in Land 

3 Cover and Terrestrial Biogeochemistry). The principal global terms in LCC are deforestation and 

4 afforestation. The increased use of satellite observations to quantify LCC has lowered recent 

5 estimates of the negative LCC RF (e.g., Ju and Masek 2016). In areas with significant irrigation, 

6 surface temperatures and precipitation are affected by a change in energy partitioning from 

7 sensible to latent heating. Direct RF due to irrigation is generally small and can be positive or 

8 negative, depending on the balance of long- wave (surface cooling or increases in water vapor) 

9 and short-wave (increased cloudiness) effects (Cook et al. 2015). 

10 CONTRAILS 

1 1 Persistent line-shaped (linear) contrails are formed in the wake of jet-engine aircraft operating in 

12 the mid to upper troposphere. Persistent contrail formation begins in the expanding exhaust 

13 plume on ambient or aircraft-induced aerosol and requires ambient ice-supersaturated conditions. 

14 As contrails spread and drift with the local winds after fonnation, they lose their linear feature 

15 while creating additional contrail cirrus cloudiness that is indistinguishable from background 

16 cloudiness. Contrails and contrail cirrus are additional forms of cirrus cloudiness, which interact 

17 with solar and thermal radiation to provide a global net positive RF and, thus, are visible 

18 evidence of an anthropogenic contribution to climate change (Burkhardt and Karcher 2012). 

19 2.4 Industrial-era Changes in Radiative Forcing Agents 

20 The best estimates of present day RFs and ERFs from principal anthropogenic and natural 

21 climate drivers are shown in Figure 2.3 and in Table 2.1. The past changes in the industrial era 

22 leading up to present day RF are shown for anthropogenic gases in Figure 2.5 and for all climate 

23 drivers in Figure 2.6. The combined figures have several striking features. First, the sum of ERFs 

24 from CO2 and non- CO 2 GHGs, tropospheric ozone, stratospheric water, contrails, and BC on 

25 snow shows a gradual, monotonic increase since 1750, with an accelerated trend in the past 50 

26 years. The sum of aerosol effects, stratospheric ozone depletion, and land use show a gradual, 

27 monotonic decrease until near the end of the 20th century, followed by decades with no further 

28 decrease. Volcanic RFs reveal their episodic, short-lived characteristics along with large values 

29 that at times dominate the total RF. 

30 Changes in total solar irradiance over the industrial era are dominated by the 1 1-year solar cycle 

3 1 and other short-term variations (Figure 2.6). Radiative forcing due to changes in solar irradiance 

32 are estimated at 0.05 (0.0 - 0.1) W/m 2 between 1745 and 2005 (Myhre et al. 2013). 

33 Inconsistencies among models, which all rely on proxies of solar irradiance to fit the industrial 

34 era, lead to the large relative uncertainty in solar RF. 

35 The atmospheric concentrations of CO2, CH4, and N2O are higher now than they have been in the 

36 past 800,000 years (Masson-Dehnotte et al. 2014). All have increased monotonically over the 

37 industrial era, and are now 40%, 250%, and 20%, respectively, above their preindustrial 


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1 concentrations as reflected in the RF time series in Figure 2.5. Tropospheric ozone has increased 

2 in response to growth in precursor emissions in the industrial era. Synthetic GHG emissions have 

3 grown rapidly beginning in the mid-20th century, with many bringing halogens to the 

4 stratosphere and causing ozone depletion in subsequent decades. Aerosol RF effects are a sum 

5 over aerosol-radiation and aerosol-cloud interactions, which increased in the industrial era due 

6 to increased emissions of aerosol and aerosol precursors. These global trends average across 

7 disparate trends in concentrations at the regional scale, and to a lesser degree temporal trends in 

8 aerosol composition. 

9 2.5 The Complex Relationship between Concentrations, Forcing, and 

10 Climate Response 

1 1 Emissions, concentrations, forcing, and climate change metrics are often discussed at the global, 

12 annual-average scale. However, all vary both geographically and seasonally with the 

13 consequence that the associated patterns of concentration, forcing, and climate change do not 

14 strictly map to each other. In particular, feedbacks (Section 2.6) either amplify or dampen the 

15 direct effects of radiative forcing, as well as affecting the geographic and temporal patterns of 

16 climate response. As such, feedbacks are one reason that forcing and the climate change caused 

17 by that forcing are not linearly related. 

18 The RF patterns of short-lived climate drivers with inhomogeneous source distributions, such as 

19 aerosols, ozone, contrails, and LCC, are leading examples of highly inhomogeneous forcings. 

20 Spatial variability in aerosol emissions is enhanced by factors associated with meteorology (for 

2 1 example, precipitation, temperature, and transport) and chemical transformation or formation 

22 (for example, primary to secondary aerosol formation). These factors highlight the additional 

23 inhomogeneity that exists, in general, in the temporal dimension. Even for relatively uniformly 

24 distributed species (for example, WMGHGs), RF patterns are less homogenous than their 

25 concentrations. The RF of a uniform CCF distribution, for example, is highly latitude and 

26 humidity dependent. With the added complexity and variability of regional forcings, the global 

27 mean RFs are known with more confidence than the regional RF patterns. 

28 Quantifying the relationship between spatial RF patterns and regional and global responses is 

29 difficult because it requires distinguishing forcing responses from the inherent internal variability 

30 of the climate system, which acts on a range of time scales. In addition, studies have shown that 

3 1 the spatial pattern and timing of climate responses are not always well correlated with the spatial 

32 pattern and timing of radiative forcing, since adjustments within the climate system can 

33 detennine much of the response (e.g., Shindell and Faluvegi 2009; Crook and Forster 2011; 

34 Knutti and Rugenstein 2015). The ability to test the accuracy of modeled responses to forcing 

35 patterns is limited by the sparsity of long-term observational records of regional climate 

36 variables. As a result there is very low confidence in our understanding of the qualitative and 

37 quantitative forcing-response relationship at the regional scale. However there is medium to high 

38 confidence in some more robust features, such as aerosol effects altering the location of the Inter 


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1 Tropical Convergence Zone (ITCZ) and the positive feedback to reductions snow and ice albedo 

2 changes at high latitudes (Boucher et al. 2013; Myhre et al. 2013). 

3 2.6 Climate-forcing Feedbacks 

4 Climate sensitivity is determined by the magnitude of the imposed forcings (ERFs) and by the 

5 climate responses to those forcings (Figure 2.2). All feedbacks can be quantified themselves as 

6 forcings, since each acts by affecting the Earth’s albedo or its greenhouse effect. The responses 

7 to radiative forcing that constitute climate feedbacks are the largest source of uncertainty in 

8 climate sensitivity (Flato et al. 2013); namely, the response of clouds, the carbon cycle and, to a 

9 lesser extent, land and sea ice to surface temperature and precipitation changes driven by ERFs. 

10 These feedbacks operate on a range of time scales, and some may not be realized for decades or 

1 1 centuries. Near-tenn and long-term feedbacks are described in the following sections. 

12 2.6.1 Near-term Feedbacks 

13 PLANCK FEEDBACK 

14 When the temperatures of Earth’s surface and atmosphere increase in response to RF, more 

15 infrared radiation is emitted into the lower atmosphere; this serves to restore radiative balance at 

16 the tropopause. This radiative feedback, defined as the Planck feedback, only partially offsets the 

17 positive RF while triggering other feedbacks that affect radiative balance. The Planck feedback 

18 magnitude is -3.20 ± 0.04 W/m 2 per 1°C wanning and is the strongest and primary stabilizing 

19 feedback in the climate system (Vial et al. 2013). 

20 WATER VAPOR AND LAPSE RATE FEEDBACKS 

21 Wanner air holds more moisture (water vapor) than cooler air — about 7% more per degree 

22 Celsius — as dictated by the Clausius-Clapeyron relationship (Allen and Igram 2002). Thus, as 

23 global temperatures increase, the total amount of water vapor in the atmosphere increases, 

24 adding further to greenhouse warming — a positive feedback, adding approximately 1 .6 W/m 2 per 

25 1°C of warming (Flato et al. 2013, Table 9.5). The water vapor feedback is responsible for more 

26 than doubling the direct climate warming from CO 2 emissions alone (Bony et al. 2006; Soden 

27 and Held 2006; Vial et al. 2013). Observations confirm that global tropospheric water vapor has 

28 increased commensurate with measured wanning (IPCC 2013, FAQ 3.2 and Figure la). 

29 Interannual variations and trends in stratospheric water vapor, while influenced by tropospheric 

30 abundances, are controlled largely by tropopause temperatures and dynamical processes (Dessler 

31 et al. 2014). Increases in tropospheric water vapor have a larger warming effect in the upper 

32 troposphere (where it is cooler) than in the lower troposphere, thereby decreasing the rate at 

33 which temperatures decrease with altitude (the lapse rate). Wanner temperatures aloft increase 

34 outgoing infrared radiation — a negative feedback. Water vapor and lapse rate feedback strengths 

35 are 1.71 ±0.13 W/m 2 per 1°C wanning and -0.66 ±0.17 W/m 2 per 1°C wanning, respectively 

36 (Vial et al. 2013). These values remain largely unchanged between recent IPCC assessments 


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1 (IPCC 2007; 2013). Recent advances in both observations and models have increased confidence 

2 that the net effect of the water vapor and lapse rate feedbacks is a significant positive RF (Flato 

3 etal. 2013). 

4 CLOUD FEEDBACKS 

5 Increases in cloudiness have two direct impacts on radiative fluxes: increased scattering of 

6 sunlight, which increases Earth’s albedo (the shortwave cloud radiative effect, SWCRE), and 

7 increased trapping of infrared radiation (the longwave cloud radiative effect, LWCRE), which 

8 warms the surface. Decreases in cloudiness have the opposite effects. The SWCRE has a larger 

9 effect on local albedo when clouds are over dark surfaces (for example, oceans) than when over 

10 higher albedo surfaces, such as sea ice and deserts. For clouds globally, the SWCRE is about -50 

1 1 W/m 2 and the LWCRE about +30 W/m 2 , yielding a net cooling influence (Loeb et al. 2009; Sohn 

12 et al. 2010). The relative magnitudes of the SWCRE and LWCRE vary with cloud type as well 

13 as with location. Low-altitude, thick clouds (for example, stratus and stratocumulus) have a net 

14 cooling, whereas high-altitude, thin clouds (for example, cirrus) have a net wanning (e.g. 

15 Hartmann et al. 1992; Chen et al. 2000). Therefore, increases in low clouds that result from RF 

16 are a negative feedback to forcing, while increases in high clouds are a positive feedback. Cloud 

17 feedbacks to RF have the potential to be significant because the potential magnitudes of cloud 

18 effects are large compared with global RF (see Section 2.4). Cloud feedbacks also influence 

19 natural variability within the climate system and may amplify atmospheric circulation patterns 

20 and the El Nino-Southern Oscillation (Radel et al. 2016). The net effect of cloud feedbacks is 

21 estimated to be positive over the industrial era, with a value of +0.27 ± 0.42 W/m 2 per 1°C 

22 wanning (Vial et al. 2013). The net cloud feedback can be broken into components, where the 

23 LW cloud feedback is positive (+0.24 ± 0.26 W/nr per 1°C warming) and the SW feedback is 

24 near-zero (+0.14 ± 0.40 W/m 2 per 1°C warming; Vial et al. 2013), though the two do not add 

25 linearly. The value of the SW cloud feedbacks shows a significant sensitivity to computation 

26 methodology (Taylor et al. 2011; Vial et al. 2013; Klocke et al. 2013). Uncertainty in cloud 

27 feedbacks remains the largest source of inter-model differences in calculated climate sensitivity 

28 (Vial et al. 2013; Boucher et al. 2013). 

29 SNOW, ICE, AND SURFACE ALBEDO 

30 Snow and ice are highly reflective of solar radiation relative to land surfaces and the ocean. Loss 

31 of snow cover, glaciers, ice sheets, and sea ice resulting from climate warming lowers Earth’s 

32 surface albedo, which increases absorbed solar radiation and leads to further warming as well as 

33 changes in turbulent heat fluxes at the surface (Sejas et al. 2014). For ice sheets (for example, on 

34 Antarctica and Greenland [Ch. 1 1 : Arctic Changes]), the positive radiative feedback is further 

35 amplified by dynamical feedbacks on ice sheet mass loss. Specifically, continental ice shelves 

36 limit the discharge rates of ice sheets into the ocean; melting of ice shelves results in an 

37 acceleration of the discharge rate and appears to be a positive feedback on the ice stream flow 

38 rate and total mass loss (e.g. Holland et al. 2008; Schoof 2010; Rignot et al. 2010; Joughin et al. 


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1 2012). Feedbacks related to ice sheet dynamics occur on longer timescales than other 

2 feedbacks — many centuries or longer. Significant ice sheet melt can also lead to changes in 

3 freshwater input to the oceans, which in turn can affect ocean temperatures and circulation, 

4 ocean-atmosphere heat exchange and moisture fluxes, and atmospheric circulation (Masson- 

5 Delmotte et al. 2014). 

6 The complete contribution of ice sheet feedbacks on timescales of millennia are not generally 

7 included in CMIP5 climate simulations. These slow feedbacks are also not thought to change in 

8 proportion to global mean surface temperature change, implying that the climate sensitivity 

9 changes with time, making it difficult to fully understand climate sensitivity considering only the 

10 industrial age. This also implies a high likelihood for tipping points, as discussed further in 

11 Chapter 15. 

12 The surface-albedo feedback is important to interannual variations in sea ice as well as to long- 

13 tenn climate change. While there is a significant range in estimates of the snow-albedo feedback, 

14 it is assessed as positive (Hall and Qu 2006; Fernandes et al. 2009; Vial et al. 2013), with a best 

15 estimate of 0.27 ± 0.06 W/m 2 per 1°C of warming globally. This feedback acts only where snow 

16 and ice are present and, thus, is most effective in polar regions (Winton 2006; Taylor et al. 

17 2011). However, there is evidence that the presence of a polar surface-albedo feedback 

18 influences the tropical climate as well (Hall 2004). 

19 Changes in sea ice can also influence Arctic cloudiness. Recent work indicates that Arctic clouds 

20 have responded to sea ice loss in fall but not summer (Kay and Gettehnan 2009; Kay et al. 2011; 

21 Taylor et al. 2015; Kay and L’Ecuyer 2013; Pistone et al. 2014). This has important implications 

22 for future climate change, as an increase in summer clouds could offset a portion of the 

23 amplifying surface albedo feedback, slowing down the rate of Arctic wanning. 

24 ATMOSPHERIC COMPOSITION 

25 Climate change can alter the atmospheric abundance and distribution of some radiatively active 

26 species by changing natural emissions, atmospheric photochemical reaction rates, atmospheric 

27 lifetimes, transport patterns, or deposition rates. These changes in turn alter the associated ERFs, 

28 leading to further climate changes (Liao et al. 2009; Unger et al. 2009; Raes et al. 2010). 

29 Important examples include climate-driven changes in temperature and precipitation that affect 

30 1) natural sources of NO x from soils and lightning and VOC sources from vegetation, all of 

3 1 which affect ozone abundances (Raes et al. 2010); 2) regional aridity, which influences surface 

32 dust sources as well as susceptibility to wildfires; and 3) surface winds, which control the 

33 emission of dust from the land surface and the emissions of sea salt and dimethyl sulfide — a 

34 natural precursor to sulfate aerosol — from the ocean surface. 

35 Feedbacks through changes in composition occur through a variety of processes. Climate-driven 

36 ecosystem changes that alter the carbon cycle potentially impact atmospheric CO2 and CH4 

37 abundances (Section 2.6.2). Atmospheric aerosols affect clouds and precipitation rates, which in 


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1 turn alter aerosol removal rates, lifetimes, and atmospheric abundances. Longwave radiative 

2 feedbacks and climate-driven circulation changes also alter stratospheric ozone abundance 

3 (Nowack et al. 2015). Investigation of these and other chemistry-climate interactions is an active 

4 area of research (e.g., John et al. 2012; Pacifico et al. 2012; Morgenstern et al. 2013; Holmes et 

5 al. 2013; Naik et al. 2013, Voulgarakis et al. 2013; Isaksen et al. 2014; Dietmuller et al. 2014; 

6 Banerjee et al. 2014). While understanding of key processes is improving, atmospheric chemistry 

7 feedbacks are absent or limited in many global climate modeling studies used to project future 

8 climate, though this is rapidly changing (https://cmip.ucar.edu/aer-chem-mip). For some 

9 chemistry-climate feedbacks involving shorter-lived constituents, the net effects may be near- 

10 zero at the global scale while significant at local to regional scales (e.g. Raes et al. 2010; Han et 

11 al. 2013). 

12 2.6.2 Long-term Feedbacks 

13 TERRESTRIAL ECOSYSTEMS AND CLIMATE CHANGE FEEDBACKS 

14 The cycling of carbon through the climate system is an important long-tenn climate feedback 

15 that affects atmospheric CO 2 concentrations. Atmospheric CO 2 concentrations are determined by 

16 emissions from burning fossil fuels, wildfires, and pennafrost thaw balanced against CO 2 uptake 

17 by the oceans and terrestrial biosphere (Ciais et al. 2013; Le Quere et al. 2016) (Figure 2.2). 

1 8 About two-thirds of anthropogenic CO 2 is taken up by the terrestrial environment and the oceans, 

19 through photosynthesis and through direct diffusion into ocean surface waters, respectively. The 

20 ability of the land to continue uptake of CO 2 is uncertain and depends on land-use management 

2 1 through mitigation and/or policy and urbanization and ocean acidification processes (see 

22 Chapters 10 and 13). Altered uptake rates will affect atmospheric CO 2 abundances, forcing, and 

23 rates of climate change. Such changes are expected to evolve on the decadal and longer time- 

24 scale, though abrupt changes are possible. 

25 Significant uncertainty exists in quantification of carbon cycle feedbacks. Differences in the 

26 assumed characteristics of the land carbon-cycle processes are the primary cause of the inter- 

27 model spread in modeling the present-day carbon cycle and a leading source of uncertainty. 

28 Significant uncertainties also exist in ocean carbon-cycle changes in future climate scenarios. 

29 Basic principles of carbon cycle dynamics in terrestrial ecosystems suggest that increased 

30 atmospheric CO 2 concentrations can directly enhance plant growth rates and, therefore, increase 

3 1 carbon uptake (the “CO 2 fertilization” effect), nominally sequestering much of the added carbon 

32 from fossil-fuel combustion (e.g., Wenzel et al. 2016). However, this effect is variable; 

33 sometimes plants acclimate so that higher CO 2 concentrations no longer enhance growth (e.g., 

34 Franks et al. 2013). In addition, CO 2 fertilization is often offset by other factors limiting plant 

35 growth, such as water and or nutrient availability, temperature, and incoming solar radiation that 

36 can be modified by changes in vegetation structure. Large-scale plant mortality through fire, soil 

37 moisture drought, and/or temperature changes also impact successional processes that contribute 


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1 to reestablishment and revegetation (or not) of disturbed ecosystems, altering the amount and 

2 distribution of plants available to uptake CO 2 . 

3 Climate-induced changes in the horizontal (for example, landscape to biome) and vertical (soils 

4 to canopy) structure of terrestrial ecosystems also alter the physical surface roughness and 

5 albedo, as well as biogeochemical (carbon, nitrogen) cycles and biophysical evapotranspiration 

6 and water demand. Combined, these responses constitute climate feedbacks by altering surface 

7 albedo and atmospheric GHG abundances. Drivers of these changes in terrestrial ecosystems 

8 include changes in the biophysical growing season, altered seasonality, wildfire patterns, and 

9 multiple other interacting factors (Chapter 10). 

10 Detennination of accurate future CO 2 stabilization scenarios depends on accounting for the 

1 1 significant role that the land biosphere plays in the global carbon cycle and feedbacks between 

12 climate change and the terrestrial carbon cycle (Hibbard et al. 2007). Earth System Models 

13 (ESMs) are increasing representation of terrestrial carbon cycle processes, including plant 

14 photosynthesis, plant and soil respiration and decomposition as well as CO 2 fertilization, with the 

15 latter based on the assumption that increased atmospheric CO 2 concentrations provide more 

16 substrate for photosynthesis and productivity. Recent advances in ESMs are beginning to 

17 account for other important factors such as nutrient limitations (Thornton et al. 2007; Brzostek et 

18 al. 2014; Wieder et al. 2015). ESMs that do include carbon-cycle feedbacks appear, on average, 

19 to overestimate terrestrial CO 2 uptake under the present-day climate (Anav et al. 2013; Smith et 

20 al. 2016) and underestimate nutrient limitations to CO 2 fertilization (Wieder et al. 2015). The 

21 sign of the land carbon-cycle feedback through 2100 remains unclear in the newest generation of 

22 ESMs (Friedlingstein et al. 2006, 2014; Wieder et al. 2015). Eleven CMIP5 ESMs forced with 

23 the same CO? emissions scenario — one consistent with RCP8.5 concentrations — produce a range 

24 of 795 to 1 145 ppm for atmospheric CO 2 concentration in 2100. The majority of the ESMs (7 out 

25 of 1 1) simulated a CO 2 concentration larger (by 44 ppm on average) than their equivalent non- 

26 interactive carbon cycle counterpart (Friedlingstein et al. 2014). This difference in CO 2 equates 

27 to about 0.2°C more warming by 2100. The inclusion of carbon-cycle feedbacks does not alter 

28 the lower-end estimate of climate sensitivity, but in most climate models it pushes the upper 

29 bound higher (Friedlingstein et al. 2014). 

30 OCEAN CHEMISTRY, ECOSYSTEM, AND CIRCULATION CHANGES 

3 1 The ocean plays a critical role in regulating climate change by controlling the amount of 

32 greenhouse gases (including CO?, water vapor, and nitrous oxide) and heat that remain in the 

33 atmosphere. The ocean also absorbs most of the net energy increase in the climate system from 

34 anthropogenic RF. This additional heat is stored predominantly (about 60%) in the upper 700 

35 meters of the ocean (Johnson et al. 2016 and see Ch. 12: Sea Level Rise and Ch. 13: Ocean 

36 Acidification). 


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1 Marine ecosystems take up CO 2 from the atmosphere in the same way that plants do on land. 

2 About half of the global net primary production (NPP) is by marine plants (approximately 50 ± 

3 28 PgC/year; Falkowski et al. 2004; Carr et al. 2006; Chavez et al. 2011). Phytoplankton NPP 

4 supports the biological pump, which transports 2-12 PgC/year of organic carbon to the deep sea 

5 (Doney 2010; Passow and Carlson 2012), where it is sequestered away from the atmospheric 

6 pool of carbon for 200-1500 years. Estimates of future changes in phytoplankton distributions 

7 and uptake of CO? vary significantly. 

8 Remote sensing of sea surface temperature and chlorophyll as well as model simulations and 

9 sediment records suggest that global phytoplankton NPP may have increased over the last 

10 century as a consequence of decadal-scale natural climate variability such as the El Nifto- 

1 1 Southern Oscillation, which promotes nutrient enrichment of the euphotic zone through vertical 

12 mixing and upwelling (Bidigare et al. 2009; Chavez et al. 2011; Zhai et al. 2013). In contrast, 

13 other analyses of chlorophyll distributions suggest that annual phytoplankton NPP in the global 

14 ocean has declined by more than 6% over the last three decades, mostly attributed to diatom 

15 changes (Gregg et al. 2003; Rousseaux and Gregg 2015). In contrast, other analyses suggest that 

16 phytoplankton NPP has decreased by about 1% per year over the last 100 years (Behrenfeld et al. 

17 2006; Boyce et al. 2010; Capotondi et al. 2012). These results are consistent with model 

18 simulations indicating that both NPP and the biological pump have decreased by 6.6% and 8%, 

19 respectively, over the last five decades (Laufkotter et al. 2015), trends that are expected to 

20 continue through the end of this century (Steinacher et al. 2010). Consistent with this result, 

2 1 carbon cycle feedbacks in the ocean were positive across the suite of CMIP5 models. 

22 In addition to being an important carbon sink, the ocean dominates the hydrological cycle, since 

23 most of the surface evaporation and rainfall occurs over the ocean (Trenberth et al. 2007; 

24 Schanze et al. 2010). The rate of evaporation, and thus the water vapor feedback, depends on 

25 surface wind stress and ocean temperature. Climate warming from radiative forcing also is 

26 associated with intensification of the water cycle (Ch. 7: Precipitation Changes). Over decadal 

27 timescales the surface ocean salinity has increased in areas of high salinity, such as the 

28 subtropical gyres, and decreased in areas of low salinity, such as the Warm Pool region (Durack 

29 and Wijfels 2010; Good et al. 2013). This increase in stratification in select regions and mixing 

30 in other regions leads to altered patterns of ocean circulation, which impacts uptake of 

3 1 anthropogenic heat and CO?. 

32 Increased ocean temperatures also affect ice sheet melt, particularly for the Antarctic Ice Sheet 

33 where basal sea ice melting is important relative to surface melting due to colder surface 

34 temperatures (Rignot and Thomas 2002). For the Greenland Ice Sheet, submarine melting at 

35 tidewater margins is also contributing to volume loss (van Den Broeke et al. 2009). In turn, 

36 changes in ice sheet melt rates change cold and fresh water inputs, altering ocean stratification. 

37 This affects ocean circulation and the ability of the ocean to absorb more greenhouse gases and 

38 heat (Enderlin and Hamilton 2014). Enhanced sea ice export to lower latitudes gives rise to local 

39 salinity anomalies (such as the Great Salinity Anomaly; Gelderloos et al. 2012) and therefore to 


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1 changes in ocean circulation and air-sea exchanges of momentum, heat, and freshwater, which in 

2 turn affect the atmospheric distribution of heat and greenhouse gases. 

3 Additionally, as the ocean warms and freshens it becomes more stratified, inhibiting surface 

4 mixing, high-latitude convection, and deep water formation, thereby weakening the Meridional 

5 Overturning Circulation (MOC), the global ocean’s conveyor belt (Kostov et al. 2014; Andrews 

6 et al. 2012; see also Ch. 13: Ocean Acidification). Reduced deep water fonnation and slower 

7 overturning are associated with decreased heat and carbon sequestration at greater depths. 

8 Sporadic observations in the 1980s, 90s, and 2000s have led to the conclusion that there already 

9 is a slowdown (Lherminier et al. 2007). Other observational studies have not found any 

10 significant slowdown (Lumpkin et al., 2008). Recent continuous observations of MOC in the 

1 1 North Atlantic show that there are no detectible trends since 2004 (Cunningham and Marsh 

12 2010). However, a recent analysis (Rahmstorf et al. 2015) finds that there has been an 

13 approximately 10% reduction in the strength of the overturning circulation over the 20th and 

14 early 21st Centuries. Future projections show that the strength of MOC will significantly 

15 decrease as the ocean warms and freshens and as upwelling in the Southern Ocean weakens due 

16 to storm track moving poleward (Rahmstorf et al. 2015; see also Ch. 13: Ocean Acidification). 

17 Such a slowdown of the ocean currents will impact the rate at which the ocean will absorb CO 2 

18 and heat from the atmosphere. 

19 PERMAFROST AND HYDRATES 

20 Permafrost and methane hydrates contain large stores of carbon in the form of organic materials, 

21 mostly at northern high latitudes. With warming, this organic material can thaw, making 

22 previously frozen organic matter available for microbial decomposition, releasing CO 2 and 

23 methane to the atmosphere, providing additional radiative forcing and accelerating warming. 

24 This process defines the permafrost-carbon feedback. Combined data and modeling studies 

25 suggest that the permafrost-carbon feedback is very likely positive (Schaefer et al. 2014; Koven 

26 et al. 2015a; Schuur et al. 2015). This feedback was not included in the IPCC projections but is 

27 an active area of research. Accounting for permafrost-carbon release reduces the amount of 

28 emissions allowable from anthropogenic sources if future GHG mitigation targets are to be met 

29 (Gonzalez-Eguino and Neumann 2016). 

30 The permafrost-carbon feedback strength indicates a 120 ± 85 Gt release of carbon from 

31 permafrost by 2100, corresponding to a global temperature increase of +0.94° ± 0.68°F (+0.52° ± 

32 0.38°C) (Schaefer et al. 2014). A key feature of the permafrost feedback is that, once initiated, it 

33 continues for an extended period because emissions from decomposition occur slowly over 

34 decades and longer. In the coming few decades, enhanced plant growth at high latitudes and its 

35 associated CO 2 sink (Friedlingstein et al. 2006) are expected to partially offset the increased 

36 emissions from permafrost thaw (Schaefer et al. 2014; Schuur et al. 2015); thereafter, 

37 decomposition will dominate uptake. Recent evidence indicates that permafrost thaw is occurring 

38 faster than expected; poorly understood deep-soil carbon decomposition and ice wedge processes 


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1 likely contribute (Koven et al. 2015b; Liljedahl et al. 2016). Chapter 1 1 includes a more detailed 

2 discussion of permafrost and methane hydrates in the Arctic. Future changes in permafrost 

3 emissions and the potential for even greater emissions from methane hydrates in the continental 

4 shelf are discussed further in Chapter 15. 

5 


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1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 Human activities continue to significantly affect Earth’s climate by altering factors that change 

4 its radiative balance (known as a radiative forcing). These factors include greenhouse gases, 

5 small airborne particles (aerosols), and the reflectivity of the Earth’s surface. In the industrial 

6 era, human activities have been and remain the dominant cause of climate warming and have far 

7 exceeded the relatively small net increase due to natural factors, which include changes in energy 

8 from the sun and the cooling effect of volcanic eruptions. 

9 Description of evidence base 

10 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

1 1 science literature, including in previous national (NCA3; Melillo et al. 2014) and international 

12 (IPCC 2013) assessments. The assertion that Earth’s climate is controlled by its radiative balance 

13 is a well-established physical property of the planet. Quantification of the changes in Earth’s 

14 radiative balance come from a combination of observations and calculations. Satellite data are 

15 used directly to observe changes in Earth’s outgoing visible and infrared radiation. Since 2002, 

16 observations of incoming sunlight include both total solar irradiance and solar spectral irradiance 

17 (Ermolli et al. 2013). Extensive in situ and remote sensing data are used to measure the 

18 concentrations of radiative forcing agents (greenhouse gases and aerosols) and changes in land 

19 cover, as well as the relevant properties of these agents (for example, aerosol microphysical and 

20 optical properties). Concentrations of long-lived greenhouse gases in particular are well- 

21 quantified through a limited number of observations because of their relatively high spatial 

22 homogeneity. Calculations of radiative forcing by greenhouse gases and aerosols are supported 

23 by observations of radiative fluxes from the surface, from airborne research platforms and from 

24 satellites. Both direct observations and modeling studies support the assertion that while 

25 volcanoes can have significant effects on climate over periods ranging from a couple of years 

26 (more moderate eruptions) to decades (very large eruptions), over the industrial era radiative 

27 forcing by volcanoes has been episodic and has not contributed significantly to forcing trends. 

28 Observations indicate a positive but small increase in solar input over the industrial era. 

29 Relatively higher variations in solar input at shorter (UV) wavelengths may be leading to indirect 

30 changes in Earth’s radiative balance through their impact on ozone concentrations that are larger 

3 1 than the radiative impact of changes in total solar irradiance, but these changes are also small in 

32 comparison to anthropogenic greenhouse gas and aerosol forcing. 

33 Major uncertainties 

34 The largest source of uncertainty regarding changes in the Earth’s radiative balance over the 

35 industrial era is quantifying forcing by aerosols. This has been a consistent finding across 

36 previous assessments (e.g., IPCC 2007; IPCC 2013). See discussion of major uncertainties 

37 associated with aerosol forcing in the Traceable Accounts for Key Finding 2 below. 


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1 Recent work has highlighted the potentially larger role of variations in UV solar irradiance, 

2 versus total solar irradiance, in solar forcing. However, this increase in solar forcing uncertainty 

3 is not sufficiently large to reduce confidence that anthropogenic activities dominate industrial- 

4 era forcing. 

5 Assessment of confidence based on evidence and agreement, including short description of 

6 nature of evidence and level of agreement 

7 x Very High 

8 □ High 

9 □ Medium 

10 □ Low 

1 1 There is very high confidence that anthropogenic radiative forcing exceeds natural forcing over 

12 the industrial era. While there remain large uncertainties in aerosol radiative forcing in particular, 

13 natural forcing through solar irradiance changes and volcanic activity has been, with very high 

14 confidence, small over the industrial era relative to anthropogenic forcing. Estimates of 

15 anthropogenic industrial-era forcing have become larger and more positive with time: from the 

16 AR4 estimate (IPCC 2007) of anthropogenic forcing for the industrial era up to 2005 to the AR5 

17 estimate (IPCC 2013) of forcing up to 201 1, ERF increased by 43%. This is due to an increase in 

1 8 positive radiative forcing from greenhouse gas concentrations and improved understanding of 

19 forcing by aerosols that led to a reduction in the estimates of their negative forcing (IPCC 2013). 

20 Summary sentence or paragraph that integrates the above information 

21 This key finding is consistent with that in IPCC AR4 (IPCC 2007) and IPCC AR5 (IPCC 2013); 

22 namely, anthropogenic radiative forcing is positive (climate wanning) and substantially larger 

23 than natural forcing from variations in solar input and volcanic emissions. Confidence in this 

24 finding has increased from AR4 to AR5, as anthropogenic greenhouse-gas forcings have 

25 continued to increase, whereas solar forcing remains small and volcanic forcing near-zero over 

26 decadal timescales. 

27 Key Finding 2 

28 Aerosols caused by human activity play a profound and complex role in the climate system 

29 through direct radiative effects and indirect effects on cloud formation and properties. The 

30 combined forcing of aerosol-radiation and aerosol-cloud interactions is negative over the 

3 1 industrial era, substantially offsetting a substantial part of greenhouse gas forcing, which is 

32 currently the predominant human contribution. The magnitude of this offset has declined in 

33 recent decades due to a decreasing trend in net aerosol forcing. 

34 


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1 Description of evidence base 

2 The Key Finding and supporting text summarize extensive evidence documented in the climate 

3 science literature, including in previous national (NCA3; Melillo et al. 2014) and international 

4 (IPCC 2013) assessments. Fundamental physics dictates that aerosols suspended in the 

5 atmosphere will scatter sunlight, and thereby reduce incoming solar radiation. Extensive in situ 

6 and remote sensing data are used to measure emission of aerosols and aerosol precursors from 

7 specific source types, the concentrations of aerosols in the atmosphere, aerosol microphysical 

8 and optical properties, and, via remote sensing, their direct impacts on radiative fluxes. Model 

9 calculations of aerosol forcing are constrained by these observations. 

10 In addition to their direct impact on radiative fluxes, aerosols also act as cloud condensation 

1 1 nuclei. Multiple observational and modeling studies have concluded that increasing the number 

12 of aerosols in the atmosphere increases cloud albedo and lifetime, adding to the negative forcing 

13 (aerosol “indirect effects”) (e.g., Twohy 2005; Lohmann and Feichter 2005; Quaas et al. 2009; 

14 Rosenfeld et al. 2014). Particles that absorb sunlight increase atmospheric heating; if they are 

15 sufficiently dark, the net effect of scattering plus absorption can be a positive top-of-atmosphere 

16 radiative forcing. However, only a few very specific types of aerosols (for example, from diesel 

17 engines) are sufficiently dark that they have a positive radiative forcing (Bond et al. 2013). 

1 8 Modeling studies, combined with observational input, have investigated the thennodynamic 

19 response to aerosol absorption in the atmosphere (the “semi-direct effects”). Depending on 

20 aerosol location relative to the clouds and other factors the resulting changes in cloud properties 

21 can have a positive or negative effect on net downward radiative flux. The best estimate is that 

22 the semi-direct effect of aerosols is negative, offsetting approximately 15% of the positive 

23 radiative forcing by absorbing aerosols (specifically, black carbon) (Bond et al. 2013). 

24 Major uncertainties 

25 Aerosol-cloud interactions in particular are the largest source of uncertainty in both aerosol and 

26 total anthropogenic radiative forcing. These include the microphysical effects of aerosols on 

27 clouds (the “indirect effects”) and changes in clouds that result from the rapid response to 

28 absorption of sunlight by aerosols (the “semi-direct effects”). This has been a consistent finding 

29 of previous assessments (e.g., IPCC 2007; IPCC 2013). Aerosols affect the Earth’s albedo by 

30 directly interacting with solar radiation (scattering and absorbing sunlight) and by affecting cloud 

3 1 properties (albedo and lifetime). Aerosol cloud effects are, in particular, the most significant 

32 single source of uncertainty in anthropogenic ERF. This is due to poor understanding of how 

33 both natural and anthropogenic aerosol emissions have changed and how changing aerosol 

34 concentrations and composition affect cloud properties (albedo and lifetime) (Boucher et al. 

35 2013; Carslaw et al. 2013). From a theoretical standpoint, aerosol-cloud these interactions are 

36 complex, and using observations to isolate the effects of aerosols on clouds is complicated by the 

37 fact that other factors (for example, the thermodynamic state of the atmosphere) also control 

38 cloud properties. Further, changes in aerosol properties and the atmospheric thennodynamic state 

39 are often correlated and interact in non-linear ways (Stevens and Feingold 2009). 


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1 While the indirect effects lead to negative forcing with high confidence, the semi-direct effects 

2 are uncertain in both sign and magnitude, but are assessed to be likely negative. 

3 Assessment of confidence based on evidence and agreement, including short description of 

4 nature of evidence and level of agreement 

5 □ Very High 

6 x High 

7 □ Medium 

8 □ Low 

9 There is very high confidence that aerosol radiative forcing is negative on a global, annually 

10 averaged basis, medium confidence in the magnitude of the aerosol radiative forcing (RF), high 

1 1 confidence that aerosol effective radiative forcing (ERF) is also, on average, negative, and low to 

12 medium confidence in the magnitude of aerosol effective radiative forcing (ERF). Lower 

13 confidence in the magnitude of the aerosol ERF is due to large uncertainties in the effects of 

14 aerosols on clouds. Combined, we assess a high level of confidence that aerosol forcing is net- 

15 negative and sufficiently large to be substantially offsetting positive greenhouse gas forcing. 

16 Improvements in emissions estimates, observations (from both surface-based networks and 

17 satellites), and modeling capability give medium to high confidence in the finding that aerosol 

18 forcing trends are decreasing in recent decades. 

19 Summary sentence or paragraph that integrates the above information 

20 This key finding parallels the findings of IPCC AR5 (Myhre et al. 2013) that aerosols on net 

21 constitute a negative radiative forcing. While significant uncertainty remains in the quantification 

22 of aerosol ERF, we assess with high confidence that aerosols offset about half of the positive 

23 forcing by anthropogenic CO 2 and about a third of the forcing by all well-mixed anthropogenic 

24 greenhouse gases. The fraction of greenhouse gas forcing that is offset by aerosols has been 

25 decreasing over recent decades, as aerosol forcing has leveled off while greenhouse gas forcing 

26 continues to increase. 

27 Key Finding 3 

28 The climate system includes a number of positive and negative feedback processes that can 

29 either strengthen (positive feedback) or weaken (negative feedback) the system’s responses to 

30 human and natural influences. These feedbacks operate on a range of timescales from very short 

3 1 (essentially instantaneous) to very long (centuries). While there are large uncertainties associated 

32 with some of these feedbacks, the net feedback effect over the industrial era has been positive 

33 (amplifying warming) and will continue to be positive in coming decades. 

34 


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1 Description of evidence base 

2 Fundamental physics dictates that the Planck feedback only partially offsets wanning by 

3 increasing emitted infrared radiation. The largest feedback, the water vapor feedback, is again 

4 dictated by fundamental physics and with very high confidence is positive, approximately 

5 doubling the direct warming due to CO 2 emissions alone. The lapse rate feedback is, also with 

6 very high confidence, negative, but only partially offsets the water vapor feedback, with the two 

7 linked by the fact that both are driven by increases in atmospheric water vapor with warming. 

8 Estimates of this feedback strength have changed little across recent assessments (IPCC 2007; 

9 IPCC 2013). The snow and ice albedo feedback is also definitively positive in sign, with the 

10 magnitude of the feedback dependent in part on timescale of interest. Assessment of its strength 

1 1 has also not significantly changed since IPCC (2007). Cloud feedbacks can be either positive or 

12 negative, depending on the sign of the change in clouds with warming (increase or decrease) and 

13 the type of cloud that changes (low or high clouds). Recent international assessments (IPCC 

14 2007; IPCC 2013) and a separate assessment specifically of feedbacks (Vial et al. 2013) all give 

1 5 best estimates of cloud feedbacks as positive on net, with uncertainty bounds allowing for a 

16 small negative feedback. Feedbacks via changes in atmospheric chemistry are an active area of 

17 research. They are not well-quantified, but are expected to be small relative to water-vapor-plus- 

18 lapse-rate, snow, and cloud feedbacks at the global scale. Carbon cycle feedbacks through 

19 changes in the land biosphere are currently of uncertain direction but uncertainties are 

20 asymmetric: they might be small and negative but could also be large and positive. Recent best 

21 estimates of ocean carbon cycle feedbacks are that they are positive, with significant uncertainty 

22 that includes allowance of a negative feedback for present-day CO 2 levels (Laufkotter et al. 

23 2015; Steinacher et al. 2010). The thaw of permafrost with climate warming also has the 

24 potential to release large stores of carbon. While this source of CO 2 is currently likely small, the 

25 pennafrost-carbon feedback is very likely positive, and as discussed in Chapter 15, could be a 

26 large positive feedback in longer term. Thus, while negative feedback processes exist the 

27 preponderance of evidence is that positive feedback processes dominate. 

28 Major uncertainties 

29 Cloud feedbacks carry the largest uncertainty of all the feedbacks, particularly on the decadal to 

30 century time-scale. This results from the fact cloud feedbacks can be either positive or negative, 

3 1 depending not only on the direction of change (more or less cloud) but also on the type of cloud 

32 affected and, to a lesser degree, the location of the cloud. 

33 Assessment of confidence based on evidence and agreement, including short description of 

34 nature of evidence and level of agreement 

35 □ Very High 

36 x High 

37 □ Medium 


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1 □ Low 

2 There is high confidence that the net effect of all feedback processes in the climate system are 

3 positive, i.e. reinforce wanning. This is based on consistency across multiple assessments, 

4 including IPCC AR5 (IPCC 2013 and references therein) of the magnitude of, in particular, the 

5 largest feedbacks in the climate system, two of which (water vapor feedback and snow/ice 

6 albedo feedback) are definitively positive in sign. While significant increases in low cloud cover 

7 with climate wanning would be a large negative feedback to warming, modeling and 

8 observational studies do not support the idea of increases, on average, in low clouds with climate 

9 wanning. 

10 Summary sentence or paragraph that integrates the above information 

1 1 The net effect of all identified feedbacks to forcing is, by best current estimates, positive and 

12 therefore reinforces climate wanning. The various feedback processes operate on different 

13 timescales with, in particular, carbon cycle and snow and ice albedo feedbacks operating on 

14 longer timelines than water vapor, lapse rate, cloud, and atmospheric composition feedbacks. 

15 


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1 TABLES 

2 Table 2.1 . Global mean RF and ERF values in 201 1 for the industrial era a 


Climate forcing agent 

Radiative forcing (W/m 2 ) 

Effective radiative 
forcing (Wm 2 ) b 

Well-mixed greenhouse gases 
(C0 2 , CH 4 , N 2 0, and halocarbons) 

+2.83 (2.54 to 3.12) 

+2.83 (2.26 to 3.40) 

T ropospheric ozone 

+0.40 (0.20 to 0.60) 


Stratospheric ozone 

-0.05 (-0.15 to +0.05) 


Stratospheric water vapor from 

ch 4 

+0.07 (+0.02 to +0.12) 


Aerosol-radiation interactions 

-0.35 (-0.85 to +0.15) 

-0.45 (-0.95 to +0.05) 

Aerosol-cloud interactions 

Not estimated 

-0.45 (-1.2 to 0.0) 

Surface albedo (land use) 

-0.15 (-0.25 to -0.05) 


Surface albedo (black carbon 
aerosol on snow and ice) 

+0.04 (+0.02 to +0.09) 


Contrails 

+0.01 (+0.005 to +0.03) 


Combined contrails and contrail- 
induced cirrus 

Not estimated 

+0.05 (0.02 to 0.15) 

Total anthropogenic 

Not estimated 

+2.3 (1.1 to 3.3) 

Solar irradiance 

+0.05 (0.0 to +0.10) 



3 a From IPCC (Myhre et al. 2013) 

4 b RF is a good estimate of ERF for most forcing agents except black carbon on snow and ice and 

5 aerosol-cloud interactions. 

6 

7 


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1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

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Chapter 2 


FIGURES 



surface 


Units 


Wm 


340 


340 


341 




latent heat 


solar absorbed 
atmosphere 


eflect 


surface 


(70,85 (15,25) 


imba ance 


0.6 

( 0 . 2 , 1 . 0 ) 


sensible 

heat 


solar absorbed 
surface 


evapo- 

ration 




thermal 
up surface 


thermal 
down surface 


398 

(394, 400) 


342 

(338, 348) 


Figure 2.1: Global mean energy budget of the Earth under present-day climate conditions. 
Numbers state magnitudes of the individual energy fluxes in watts per square meter (W/m 2 ) 
averaged over Earth’s surface, adjusted within their uncertainty ranges to balance the energy 
budgets of the atmosphere and the surface. Numbers in parentheses attached to the energy fluxes 
cover the range of values in line with observational constraints. These constraints are largely 
provided by satellite-based observations, which have directly measured solar and infrared fluxes 
at the top of the atmosphere over nearly the whole globe since 1984 (Barkstrom, 1984; Smith et 
al., 1994). More advanced satellite-based measurements focusing on the role of clouds in Earth’s 
radiative fluxes have been available since 1998 (Wielicki et al., 1995 & 1996). (Figure source: 
Hartmann et al. 2013; © IPCC, used with permission). 


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Chapter 2 


1 

2 

3 

4 

5 

6 

7 

8 


Climate system modeling framework 



Figure 2.2: Simplified conceptual modeling framework for the climate system as implemented 
in many climate models (Chapter 4). Modeling components include forcing agents, feedback 
processes, carbon uptake processes and radiative forcing and balance. The lines indicate physical 
interconnections (solid lines) and feedback pathways (dashed lines). Principal changes (blue 
boxes) lead to climate impacts (red box) and feedbacks. (Figure source: adapted from Knutti and 
Rugenstein 2015). 


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Chapter 2 


Radiative Forcing of Climate Between 1750 and 201 1 


1 

2 

3 

4 

5 

6 

7 


Forcing agent 



-1 


Radiative Forcing (W/m 2 ) 

Figure 2.3: Bar chart for RF (hatched) and ERF (solid) for the period 1750-2011, where the 
total ERF is derived from IPCC. Uncertainties (5% to 95% confidence range) are given for RF 
(dotted lines) and ERF (solid lines). Volcanic forcing is not shown because this forcing is 
negligible over the industrial era. (Figure source: Myhre et al. 2013; © IPCC, used with 
permission). 


115 


1 

2 

3 

4 

5 

6 

7 

8 

9 


CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE 


Chapter 2 




U) 

c 

o 

i— 

o 

LJ. 

0 

> 


T3 

(0 

cc 


Figure 2.4: Atmospheric concentrations of carbon dioxide, methane and nitrous oxide over the 
last 10,000 years (large panels) and since 1750 (inset panels). Measurements are shown from ice 
cores (symbols with different colors for different studies) and atmospheric samples (red lines). 
The corresponding radiative forcings are shown on the right hand axes of the large. The 
concentrations of these gases have continued to increase in the 2000 to 2016 period 
(http://www.esrl.noaa.gov/gmd/ccgg/aggi.html). (Figure source: IPCC 2007; © IPCC, used with 
permission) 


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Chapter 2 


Radiative Forcing of Well-mixed Greenhouse Gases 


1 

2 

3 

4 

5 

6 



1850 1900 1950 2000 


Figure 2.5: (a) Radiative forcing (RF) from the major WMGHGs and groups of halocarbons 
(Others) from 1850 to 201 1; (b) the data in (a) with a logarithmic scale; (c) RFs from the minor 
WMGFIGs from 1850 to 201 1 (logarithmic scale); (d) rate of change in forcing from the major 
WMGHGs and halocarbons from 1850 to 201 1. (Figure source: Myhre et al. 2013; © IPCC, used 
with permission). 


117 


1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 


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Chapter 2 


Time Evolution of Forcings 


cn 

c 

o 


<D 

> 

OB 

ij 

(0 

cr 

<D 

> 

o 

s£ 

LU 



« 



y 

I 

Year 2011 


Figure 2.6: Effective radiative forcing changes across the industrial era for anthropogenic and 
natural forcing mechanisms. Also shown are the sum of all forcings (Total) and the sum of 
anthropogenic forcings (Total Anthropogenic). Bars with the forcing and uncertainty ranges (5 to 
95% confidence range) at present are given in the right part of the figure. For aerosol the ERF 
due to aerosol-radiation interaction and total aerosol ERF are shown. The uncertainty ranges are 
for present (2011 versus 1750) and are given in Table 8.6. For aerosols, only the uncertainty in 
the total aerosol ERF is given. For several of the forcing agents the relative uncertainty may be 
larger for certain time periods compared to present. See IPCC AR5 Supplementary Material 
Table 8.SM.8 for further information on the forcing time evolutions. Forcing numbers provided 
in Annex II of this report. The total anthropogenic forcing was 0.57 (0.29 to 0.85) W m 2 in 
1950, 1.25 (0.64 to 1.86) W/m 2 in 1980 and 2.29 (1.13 to 3.33) W/m 2 in 2011. (Figure source: 
Myhre et al. 2013; © IPCC, used with permission). 


118 



1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 


CSSR TOD: DO NOT CITE, QUOTE, OR DISTRIBUTE 


Chapter 2 


E O 

CD O) 

O CL 

c g 
” o 

CD '(/) 

f 1 

C/5 0) 

o - 
£ 8 




Figure 2.7: CO 2 sources and sinks (PgC/yr 1 ) over the industrial era (1750-2011). The 
partitioning of atmospheric emissions among the atmosphere, land and ocean is shown as 
equivalent negative emissions in the lower panel; of these, the land and ocean terms are true 
si nk s of atmospheric CO 2 . The top panel shows an expanded view of emissions from fossil fuels 
and cement manufacturing. The atmospheric CO 2 growth rate is derived from atmospheric 
observations and ice core data. The ocean CO 2 sink is derived from a combination of models and 


observations. The land sink is the residual of the other terms in a balanced CO 2 budget, and 
represents the sink of anthropogenic CO 2 in natural land ecosystems. These terms only represent 
changes since 1750 and do not include natural CO 2 fluxes (for example, from weathering and 
outgassing from lakes and rivers). (Figure source: Ciais et al. 2013; © IPCC, used with 
permission) 


119 


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Chapter 2 


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26 multi-model analysis. Biogeosciences, 7, 979-1005. http://dx.doi.org/10.5194/bg-7-979-2010 

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34 climate sensitivity estimates. Climate Dynamics, 41, 3339-3362. 

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3 Doherty, V. Eyring, G. Faluvegi, G.A. Folberth, L.W. Horowitz, B. Josse, I.A. MacKenzie, 

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1 3. Detection and Attribution of Climate Change 

2 Key Findings 

3 1 . The likely range of the human contribution to the global mean temperature increase over the 

4 period 1951-2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed wanning of 

5 1.2°F (0.65°C) (high confidence). It is extremely likely that more than half of the global mean 

6 temperature increase since 1951 was caused by human influence on climate (high 

7 confidence). The estimated influence of natural forcing and internal variability on global 

8 temperatures over that period is minor (high confidence) 

9 3.1 Introduction 

10 Detection and attribution of climate change involves assessing the causes of observed changes in 

1 1 the climate system through systematic comparison of climate models and observations using 

12 various statistical methods. Attributing an observed change or an event partly to a causal factor 

13 (such as anthropogenic climate forcing) normally requires that the change first be detectable 

14 (Hegerl et al. 2010). A detectable change is one in which an observed change is distinguishable 

15 from natural variability in some defined statistical sense, again without necessarily ascribing a 

16 cause. An attributable change refers to a change in which the relative contribution of causal 

17 factors has been evaluated along with an assignment of statistical confidence (e.g., Bindoff et al. 

18 2013; Hegerl et al. 2010). 

19 More confident statements about attribution are underpinned by a thorough understanding of the 

20 physical processes involved. Since the release of the Intergovernmental Panel on Climate 

21 Change’s Fifth Assessment Report (IPCC AR5) and the Third National Climate Assessment 

22 (NCA3; Melillo et al. 2014), there have been some advances in the science of detection and 

23 attribution of climate change. The IPCC AR5 presented an assessment of detection and 

24 attribution research at the global to regional scale (Bindoff et al. 2013) which is briefly 

25 summarized here. An emerging area in the science of detection and attribution is the attribution 

26 of extreme weather and climate events (NAS 2016; Stott 2016; Easterling et al. 2016). 

27 A growing number of climate change and extreme event attribution studies use a multi-step 

28 attribution (Hegerl et al. 2010) or attribution without detection approaches. These are methods 

29 that attribute a climate change or a change in the likelihood of occurrence of an event to a causal 

30 factor without detecting a change in the phenomenon itself. Detection, for example, would mean 

3 1 demonstrating that a long-tenn trend or change in a phenomenon is highly unusual compared to 

32 natural variability. For the multi-step approach, the attribution may be based on a change in 

33 climate conditions that are closely related to a given type of event. As an example, some 

34 attribution statements for phenomena such as droughts or hurricane activity — where there are not 

35 necessarily detectable trends — are based on models and on detected changes in related variables 

36 such as surface temperature, as well as an understanding of the relevant physical processes. 


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Possible anthropogenic influence on an extreme event can be assessed using a risk-based 
approach, which examines whether the odds of occurrence of a type of extreme event have 
changed, or through an ingredients-based or conditional attribution approach. In the latter case, 
for example, an investigator may look for changes in occurrence of atmospheric circulation and 
weather patterns relevant to the extreme event, or at the impact of certain environmental changes 
(for example, greater atmospheric moisture) on the character of an extreme event (Trenberth et 
al. 2015; Shepherd 2016; Horton et al. 2016). An example of the conditional attribution 
approach, as applied to Hurricane Sandy, assumes that the weather patterns in which the storm 
was embedded, and the stonn itself, could have occurred in a preindustrial climate, and the event 
is re-simulated changing only some aspects of the large-scale environment (for example, sea 
surface temperatures, atmospheric temperatures and moisture) by an estimated anthropogenic 
climate change signal. One study using this approach found that anthropogenic climate change to 
date did not have a statistically significant influence on the intensity of Hurricane Sandy 
(Lackmann 2015). 

There are reasons why attribution without detection statements can be appropriate, despite the 
lower confidence typically associated with such statements as compared to attribution statements 
that are supported by detection of a change in the phenomenon itself. The event may be so rare 
that a trend analysis for similar events is not practical. Including attribution without detection 
events in analysis of climate change impacts reduces the chances of a false negative, that is, 
incorrectly concluding that climate change had no influence on a given extreme events 
(Anderegg et al. 2014) in a case where it did have an influence. However, avoiding this type of 
error through attribution without detection comes at the risk of increasing the rate of false 
positives, where one incorrectly concludes that anthropogenic climate change had a certain type 
of influence on an extreme event when in fact it did not have such an influence. 

Review of Key Detection and Attribution Findings in IPCC AR5 

Key attribution assessment results for global mean temperature are summarized in Figure 3.1 
(from Bindoff et al. 2013), which shows assessed likely ranges and midpoint estimates for 
several factors contributing to increases in global mean temperature. According to Bindoff et al., 
it is extremely likely that anthropogenic forcings caused more than half of the warming for 
1951-2010, with a likely contribution range of 0.6° to 0.7°C (1.1°F to 1.3°F), compared with the 
observed warming of about 0.65°C (1.2°F). The estimated likely contribution ranges for natural 
forcing and internal variability were both much smaller (-0.1° to 0.1 °C, or -0.2° to 0.2°F). 

[INSERT FIGURE 3.1 HERE: 

Figure 3.1: Attributable warming likely ranges (bar- whisker plots) and midpoint values (colored 
bars) for global mean temperature trends (degrees Celsius) over 1951-2010 from IPCC AR5 

(Bindoff et al. 2013). Observations are from HadCRUT4, along with observational uncertainty 

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combined. The ranges within which the true value is extremely likely to occur are broader than 
the likely ranges shown in the figure, which is why the term “more than half’ is used to 

characterize the fraction of warming that is extremely likely due on anthropogenic influence, 

despite the fact that the midpoint of the likely range of the anthropogenic forcing contribution is 
close to the observed warming value. Likely ranges are broader for contributions from well- 
mixed greenhouse gases or other anthropogenic forcings, assessed separately, than for the 
contributions from all anthropogenic forcings, as it is more difficult to quantitatively constrain 

the separate contributions of the various anthropogenic forcing agents. (Figure source: redrawn 
from Bindoff et al. 2013; © IPCC. Used with permission.)] 

Likely or very likely attributable human contributions have also been reported by IPCC AR5 for 
warming over all continents except Antarctica, and, globally, changes in daily temperature 
extremes, ocean surface and subsurface temperature and salinity, and sea level pressure patterns; 
Arctic sea ice loss; northern hemispheric snow cover decrease; global mean sea level rise; and 
ocean acidification (Bindoff et al. 2013). IPCC AR5 also reported medium confidence in 
anthropogenic contributions to increased atmospheric specific humidity, zonal mean 
precipitation over northern hemisphere mid to high latitudes, and intensification or heavy 
precipitation over land regions. IPCC AR5 had weaker attribution conclusions than IPCC AR4 
on some phenomena, including tropical cyclone and drought changes. The present assessment 
does not change any of the IPCC AR5 conclusions, although we make some additional 
attribution statements in the relevant chapters of this report regarding regional temperature, 
extreme precipitation, and flooding frequency increases over parts of the United States. 

3.2 Extreme Event Attribution 

Attribution of extreme weather events under a changing climate is an important aspect of climate 
science. The European heat wave of 2003 (Stott et al. 2004) and Australia’s extreme 
temperatures and heat indices of 2013 (e.g., Arblaster et al. 2014; King et al. 2014; Knutson et al. 
2014; Lewis and Karoly 2014; Perkins et al. 2014) are examples of extreme weather or climate 
events where relatively strong evidence for a human contribution to the event has been found for 
cases outside of the United States. The science of event attribution for weather and climate 
extremes over the United States has also significantly evolved since the NCA3. For example, 
following several extreme climate events, such as the 2011 Texas heat wave and drought or the 
recent/ongoing California drought, investigators have attempted to determine, using various 
methods discussed in this chapter, whether human-caused climate change contributed to the 
event. Several recent reports have extensively reviewed the topic of extreme event attribution 
(NAS 2016; Easterling et al. 2016). While this topic cannot be comprehensively reviewed here, a 
few highlighted statements from the National Academy of Sciences study (NAS 2016) are given 
here: 


• Event attribution is more reliable when based on sound physical principles, consistent 
evidence from observations, and numerical models that can replicate the event. 


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• Confidence in attribution findings of anthropogenic influence is greatest for extreme 
events that are related to an aspect of temperature. 

• Statements about attribution are sensitive to the way the questions are posed (that is, 
framing) 

In addition, the National Academies noted that conclusions would be more robust in cases where 
observed changes in the event being examined are consistent with expectations from model- 
based attribution studies. Typically, there is less confidence in such an attribution-without- 
detection statement than one where a detectable anthropogenic influence (for example, a 
detectable and attributable long-term trend or increase in variability) on the phenomenon itself 
had also been demonstrated. An example would be stating that a change in the probability or 
magnitude of a heat wave in the southeastern United States was attributable to greenhouse gases 
when there is not a detectable trend in either long-term temperature or in temperature variability 
in the data in that region, as discussed below. No extreme weather event observed to date has 
been found to have zero probability of occurrence in a preindustrial climate according to climate 
model simulations. Therefore, the causes of attributed extreme events are a combination of 
natural variations in the climate system compounded (or alleviated) by the anthropogenic change 
to the climate system. Event attribution statements quantify the relative contribution of these 
human and natural causal factors. 

As an example illustrating different methods of event attribution, for the 2011 Texas heat 
wave/meteorological drought, Hoerling et al. (2013) found that the event was primarily caused 
by antecedent and concurrent negative rainfall anomalies due mainly to natural variability and 
the La Nina conditions at the time of the event, but with a relatively small (not detected) 
wanning contribution from anthropogenic forcing. The anthropogenic contribution nonetheless 
doubled the chances of reaching a new temperature record in 201 1 compared to the 1981-2010 
reference period, according to their study. Rupp et al. (2012), meanwhile, concluded that extreme 
heat events in Texas were about 20 times more likely for 2008 La Nina conditions than similar 
conditions during the 1960s. This pair of studies illustrates how the framing of the attribution 
question can matter. The Hoerling et al. analysis focused more on what caused most of the 
magnitude of the anomalies, whereas Rupp et al. focused more on the changes in the probability 
of the event. Otto et al. (2012) show how such approaches can give seemingly conflicting results 
yet have no fundamental contradiction. In this case, we conclude that there is medium confidence 
that anthropogenic forcing contributed to the Texas heat wave of 201 1, both in terms of a small 
contribution to the anomaly magnitude and a significant increase in the probability of occurrence 
of the event. 

In this report, we do not assess all individual weather or climate extreme events for which an 
attributable anthropogenic climate change has been claimed in a published study, as there are 
now many such studies. A few selected individual United States studies are discussed in more 


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1 detail either in this chapter or in Chapters 6, 7, 8, and 9, which focus on particular weather and 

2 climate phenomena. 

3 3.3 Updated Detection and Attribution Summaries 

4 In general, detection and attribution at regional scales are more challenging than at the global 

5 scale for a number of reasons. Regional changes typically have smaller signal-to-noise ratios 

6 than changes at global scales. Also, there is less spatial pattern information for distinguishing 

7 contributions from different forcings. Omitted forcings in climate models, such as land-use 

8 change, could be more important at regional scales, and simulated internal variability may be less 

9 reliable (Bindoff et al. 2013). 

10 In the various phenomena chapters of this report, updated detection attribution statements 

1 1 focusing on the United States region are presented. 

12 


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1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 The likely range of the human contribution to the global mean temperature increase over the 

4 period 1951-2010 is 1.1° to 1.3°F (0.6° to 0.7°C), which is close to the observed wanning of 

5 1.2°F (0.65°C) (high confidence). It is extremely likely that more than half of the global mean 

6 temperature increase since 1951 was caused by human influence on climate (high confidence). 

7 The estimated influence of natural forcing and internal variability on global temperatures over 

8 that period is minor (high confidence) 

9 Description of evidence base 

10 This Key Finding summarizes key detection and attribution evidence documented in the climate 

1 1 science literature and in the IPCC AR5 (Bindoff et al. 2013), and references therein. The Key 

12 Finding is essentially the same as the summary assessment of IPCC AR5. The attribution of 

13 temperature increases since 1951 is based on the detection and attribution analyses of Gillett et 

14 al. (2013), Jones et al. (2013), and consideration of Ribes and Terray (2013), Huber and Knutti 

15 (2011), Wigley and Santer (2013), and IPCC AR4 (Hegerl et al. 2007). The estimated potential 

16 influence of internal variability is based on Knutson et al. (2013) and Huber and Knutti (2011), 

17 with consideration of the above references. Moreover, simulated global temperature multidecadal 

18 variability is assessed to be adequate (Bindoff et al. 2013), with high confidence that models 

19 reproduce global and northern hemisphere temperature variability across a range of timescales 

20 (Flato et al. 2013). Further support for these assessments comes from paleoclimate data (Masson- 

21 Delmotte et al. 2013) and physical understanding of the climate system (IPCC 2013). A more 

22 detailed traceable account is contained in Bindoff et al. (2013). Post IPCC AR5 supporting 

23 evidence includes additional analyses showing unusual nature of observed global warming 

24 compared to simulated internal climate variability (Knutson et al., in press) and recent 

25 occurrence of new record high global mean temperatures, consistent with model projections of 

26 continued warming on multidecadal scales (for example, Chapter 1). 

27 Major uncertainties 

28 The transient climate response (TCR) is defined as the global mean surface temperature change 

29 at the time of CO 2 doubling in a 1 %/year CO 2 transient increase experiment. The TCR of the 

30 climate system to greenhouse gas increases remains uncertain, with ranges of 0.9° to 2.0°C (1.6° 

31 to 3.6°F) and 0.9° to 2.5°C (1.6° to 4.5°F) in two recent assessments (Otto et al. 2013 and Lewis 

32 and Curry 2014, respectively). The climate system response to aerosol forcing (direct and 

33 indirect effects combined) remains highly uncertain (Myhre et al. 2013), because although more 

34 of the relevant processes are being in included in models, confidence in these representations 

35 remains low (Boucher et al. 2013). Therefore, there is considerable uncertainty in quantifying the 

36 attributable warming contributions of greenhouse gases and aerosols separately. There is 

37 uncertainty in the possible levels of internal climate variability, but current estimates (likely 

38 range of +/- 0.1°C, or 0.2°F, over 60 years) would have to be too low by more than a factor or 


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1 two or three for the observed trend to be explainable by internal variability (e.g., Knutson et al. 

2 2013; Huber and Knutti 201 1). 

3 Assessment of confidence based on evidence and agreement, including short description of 

4 nature of evidence and level of agreement 

5 x Very High 

6 x High 

7 □ Medium 

8 □ Low 

9 There is very high confidence that global temperature has been increasing and that anthropogenic 

10 forcings have played a major role in the increase observed over the past 60 years, with strong 

1 1 evidence from several studies using well-established detection and attribution techniques. There 

12 is high confidence that the role of internal variability is minor, as climate models simulate only a 

13 minor role and the models have been assessed as adequate for the purpose of estimating the 

14 potential role of internal variability. 

15 If appropriate, estimate likelihood of impact or consequence, including short description of 

16 basis of estimate 

17 X Greater than 9 in 10 / Very Likely 

18 □ Greater than 2 in 3 / Likely 

19 □ About 1 in 2 / As Likely as Not 

20 □ Less than 1 in 3 / Unlikely 

21 □ Less than 1 in 10 / Very Unlikely 

22 Summary sentence or paragraph that integrates the above information 

23 Detection and attribution studies, climate models, observations, paleoclimate data, and physical 

24 understanding lead to high confidence ( extremely likely ) that more half of the observed global 

25 mean wanning since 1951 was caused by humans, and high confidence that internal climate 

26 variability played only a minor role (and possibly even a negative contribution) in the observed 

27 wanning. The key message and supporting text summarizes extensive evidence documented in 

28 the peer-reviewed detection and attribution literature, including in the IPCC AR5. 

29 


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3 

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9 

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12 

13 

14 

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FIGURES 



NAT 


Internal Variability 

M M | m m | M M | i i i i | m i i | M m | i i M | M M 

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 

(°F) 

GHG - well-mixed greenhouse gases OA - other anthropogenic forcings 

ANT - all anthropogenic forcings combined NAT - natural forcings 

Figure 3.1: Attributable warming likely ranges (bar- whisker plots) and midpoint values (colored 
bars) for global mean temperature trends (degrees Celsius) over 1951-2010 from IPCC AR5 
(Bindoff et al. 2013). Observations are from HadCRUT4, along with observational uncertainty 
(5% to 95%) error bars (Morice et al. 2012). GHG refers to well-mixed greenhouse gases, OA to 
other anthropogenic forcings, NAT to natural forcings, and ANT to all anthropogenic forcings 
combined. The ranges within which the true value is extremely likely to occur are broader than 
the likely ranges shown in the figure, which is why the tenn “more than half’ is used to 
characterize the fraction of warming that is extremely likely due on anthropogenic influence, 
despite the fact that the midpoint of the likely range of the anthropogenic forcing contribution is 
close to the observed warming value. Likely ranges are broader for contributions from well- 
mixed greenhouse gases or other anthropogenic forcings, assessed separately, than for the 
contributions from all anthropogenic forcings, as it is more difficult to quantitatively constrain 
the separate contributions of the various anthropogenic forcing agents. (Figure source: redrawn 
from Bindoff et al. 2013; © IPCC. Used with permission.) 


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i 4. Climate Models, Scenarios, and Projections 


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KEY FINDINGS 

1 . Merely maintaining present-day levels of greenhouse (heat-trapping) gases in the 
atmosphere would commit the world to at least an additional 0.3°C (0.5°F) of warming 
over this century relative to today (high confidence). Projections over the next three 
decades differ modestly, primarily due to uncertainties in natural sources of variability. 
Past mid-century, the amount of climate change depends primarily on future emissions 
and the sensitivity of the climate system to those emissions. 

2. Atmospheric carbon dioxide (CO 2 ) levels have now passed 400 ppm, a concentration last 
seen about 3 million years ago, when average temperature and sea level were 
significantly higher than today. Continued growth in CO 2 emissions over this century and 
beyond would lead to concentrations not experienced in tens to hundreds of millions of 
years. The rapid present-day emissions rate of nearly 10 GtC per year, however, suggests 
that there is no precise past climate analogue for this century any time in at least the last 
66 million years. (Medium confidence) 

3. The observed acceleration in carbon emissions over the past 15-20 years is consistent 
with higher future scenarios (very high confidence). Since 2014, growth rates have 
slowed as economic growth begins to uncouple from carbon emissions (medium 
confidence) but not yet at a rate that, were it to continue, would limit atmospheric 
temperature increase to the 2009 Copenhagen goal of 2°C (3.6°F), let alone the 1.5°C 
(2.7°F) target of the 2015 Paris Agreement (high confidence). 

4. Combining output from global climate models and dynamical and statistical downscaling 
models using advanced averaging, weighting, and pattern scaling approaches can result in 
more relevant and robust future projections. These techniques also allow the scientific 
community to provide better guidance on the use of climate projections for quantifying 
regional-scale impacts ( medium to high confidence). 

4.1. The Human Role in Future Climate 

The Earth’s climate, past and future, is not static; it changes in response to both natural and 
anthropogenic drivers (see Ch. 2: Scientific Basis). Since the industrial era, human emissions of 
carbon dioxide (CO 2 ), methane (CH 4 ), and other greenhouse gases now overwhelm the influence 
of natural drivers on the external forcing of the Earth’s climate (see Ch. 3: Detection and 
Attribution). For this reason, projections of changes in Earth’s climate over this century and 
beyond focus primarily on its response to emissions of greenhouse gases, particulates, and other 
radiatively-active species from human activities. 


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1 Climate change and ocean acidification (see Ch. 13: Ocean Acidification) are already occurring 

2 due to the buildup of atmospheric CO 2 in the industrial era (Hartmann et al. 2013; Rhein et al. 

3 2013). If atmospheric levels of greenhouse gases were frozen at current levels, temperature 

4 would continue to increase by an estimated 0.3°C (0.54°F) over this century (Collins et al. 2013). 

5 However, climate change over this century and beyond is primarily a function of future 

6 emissions and the response of the climate system to those emissions (see Ch. 2: Scientific Basis). 

7 For that reason, climate projections are not predictions; instead, they consist of a range of 

8 plausible scenarios or pathways that can be expressed in tenns of population, energy sources, 

9 technology, emissions, atmospheric concentrations, radiative forcing, and/or global temperature 

10 change. For a given scenario, it is possible to estimate the range in potential climate change — as 

1 1 detennined by climate sensitivity, which is the response of global temperature to a natural or 

12 anthropogenic forcing (see Ch. 2: Scientific Basis) — that would result at the global and regional 

13 scale (Collins et al. 2013). 

14 Over the past 15-20 years, growth rates in carbon emissions from human activities of 3%-4% 

15 per year largely tracked with those projected under higher scenarios, in large part to growing 

16 contributions from developing economies (Raupach et al. 2007; Le Quere et al. 2009). Since 

17 2014, however, growth rates have flattened, a trend cautiously attributed to declining coal use in 

18 China, despite large uncertainties in emissions reporting (Jackson et al. 2016; Korsbakken et al. 

19 2016). Carbon emissions and economic growth may be beginning to decouple, as global 

20 economies led by China and the United States phase out coal and begin the transition to 

21 renewable, non-carbon energy (IEA 2016; Green and Stem 2016). In the 2015 Paris Agreement, 

22 signatories agree to “holding the increase in the global average temperature to well below 2°C 

23 (3.6°F) above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5°C 

24 (2.7°F) above preindustrial levels” (UNFCCC 2015). To stabilize climate, however, it is not 

25 enough to halt the growth in annual carbon emissions; global net carbon emissions would 

26 eventually need to reach zero (Collins et al. 2013) and most recent economic scenarios require 

27 negative emissions for a greater than 50% chance of limiting warming below 2°C (3.6°F) (Smith 

28 et al. 2016; see also Ch. 14 Mitigation for a discussion of negative emission technologies). 

29 4.2. Future Scenarios 

30 4.2.1. Representative Concentration Pathways 

3 1 Over the last 25 years, the climate modeling community has based its simulations on standard 

32 sets of scenarios that correspond with possible future emissions of greenhouse gases, aerosols, 

33 and other species. Developed by the integrated assessment modeling community, these sets of 

34 standard scenarios have become more comprehensive with each new generation: the IS92 

35 emission scenarios of the 1990s (Leggett et al. 1992); after 2000, the Special Report on Emission 

36 Scenarios (SRES; Nakicenovic et al. 2000); and today, the Representative Concentration 

37 Pathways (RCPs; Moss et al. 2010). 


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1 The SRES scenarios began with a storyline that lays out a consistent picture of demographics, 

2 international trade, flow of information and technology; these assumptions are then fed through 

3 socioeconomic and Integrated Assessment Models (IAMs) to derive emissions. In turn, 

4 emissions were used as input to carbon cycle or earth system models to calculate resulting 

5 atmospheric concentrations and radiative forcing. In contrast, RCP scenarios are tied to one 

6 value: the change in radiative forcing at the tropopause by 2100. The four RCPs are numbered 

7 according to specific changes in radiative forcing at the tropopause from preindustrial conditions 

8 to 2100: +2.6, +4.5, +6.0 and +8.5 watts per square meter (W/m 2 ). From this value, it is possible 

9 to work backwards to derive a range of emissions trajectories and corresponding policies and 

10 technological strategies that would achieve the same ultimate impact on radiative forcing. 

1 1 Although there are multiple emissions pathways that would lead to the same radiative forcing 

12 target, an associated pathway of annual carbon dioxide and other anthropogenic emissions of 

13 greenhouse gases, aerosols, air pollutants, and other short-lived species has been identified for 

14 each RCP to use as input to future climate model simulations (e.g., Riahi et al. 2011; Cubasch et 

15 al. 2013). In addition, RCPs provide climate modelers with gridded trajectories of land use and 

16 land cover. Using the RCPs as input, climate models produce trajectories of future climate 

17 change including global and regional changes in temperature, precipitation, and other physical 

18 characteristics of the climate system (Collins et al. 2013; Kirtman et al. 2013; see also Ch. 6-7). 

19 Within the RCP family, individual scenarios have no likelihood attached to them. Higher- 

20 numbered scenarios correspond to higher emissions, and a larger and more rapid global 

21 temperature change (Figure 4. 1); the range of values covered by the scenarios was chosen to 

22 reflect the then-current range in the open literature. Since the choice of scenario constrains the 

23 magnitudes of future changes, most assessments (including this one; see Ch. 6: Temperature 

24 Change) quantify the impacts under a range of future scenarios that reflect the uncertainty in the 

25 consequences of human choices over the coming century. 

26 The higher RCP8.5 scenario corresponds to a future where carbon emissions continue to rise as a 

27 result of fossil fuel use, albeit with significant declines in emission growth rates over the second 

28 half of the century (Figure 4.1) and modest improvements in energy intensity and technological 

29 change (Riahi et al. 2011). Atmospheric carbon dioxide levels rise from current-day levels of 400 

30 up to 936 parts per million (ppm) and global temperature increases by 3° to 5.5°C (5.4° to 9.9°F) 

31 by 2 100 relative to the 1986-2005 average. RCP8.5 reflects the upper range of the open 

32 literature on emissions, but is not intended to serve as an upper limit on possible emissions nor as 

33 a business as usual or reference scenario for the other three scenarios. 

34 Projections based on SRES scenarios, such as those used in the Second and Third National 

35 Climate Assessments (NCA2 and NCA3; Karl et al. 2009; Melillo et al. 2014), are not 

36 necessarily incompatible with new RCP-based ones; RCP8.5 is similar to SRES Alfi, RCP6.0 is 

37 similar to SRES A1B, and RCP4.5 is similar to SRES B 1 . While none of the SRES scenarios 


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included a scenario with explicit policies and measures to limit climate forcing, however, the 
three lower RCP scenarios (2.6, 4.5, and 6.0) are climate-policy scenarios. 

Under the RCP4.5 and 2.6 scenarios, for example, atmospheric CO 2 levels remain below 550 and 
450 ppm by 2100, respectively. The RCP2.6 scenario is much lower than any SRES scenario 
because it includes the option of using policies to achieve net negative carbon dioxide emissions 
before the end of the century, while SRES scenarios do not. The lower the atmospheric 
concentrations of CO 2 , the greater the chance that projected global temperature change will 
remain below 2°C (3.6°F) relative to preindustrial levels, consistent with the Paris Agreement. 
Under RCP4.5, global temperature change is more likely than not to exceed 2°C (3.6°F) 
(https://tntcat.iiasa.ac. at/RcpDb/dsd?Action=htmlpage&page=compare; Collins et al. 2013), 
whereas under RCP2.6 it is likely to remain below 2°C (Sanderson et al. 2016; Collins et al. 
2013). RCPs do not consider climate forcing in the range of 2.0 W/m 2 , a level consistent with 
limiting global mean surface temperature change to 1.5°C (2.7°F); it is estimated that a 66% 
chance of achieving this target would require net zero greenhouse gas emissions by 2050 — or 
2060, if global temperature is permitted to temporarily exceed 1.5°C for up to 50 years 
(Sanderson et al. 2016). 

[INSERT FIGURE 4.1 HERE: 

Figure 4.1: The climate projections used in this report are based on the 2010 Representative 
Concentration Pathways (RCP, right). They are largely consistent with scenarios used in 
previous assessments, the 2000 Special Report on Emission Scenarios (SRES, left). This figure 
compares SRES and RCP annual carbon emissions (top), carbon dioxide equivalent levels in the 
atmosphere (middle), and temperature change that would result from the central estimate (lines) 
and the likely range (shaded areas) of climate sensitivity (bottom). (Data from CMIP3 and 
CMIP5). (Figure source: Walsh et al. 2014)] 

4.2.2. Shared Socioeconomic Pathways 

Shared Socioeconomic Pathways (SSPs) are a set of socioeconomic scenarios that include 
assumptions regarding demographics, urbanization, economic growth and technology 
development. These scenarios were designed to meet the needs of the impacts, adaptation, and 
vulnerability (IAV) communities, enabling them to explore the socioeconomic challenges to 
emissions mitigation and adaptation to climate change (O’Neill et al. 2014). Five SSP scenarios 
have been developed: SSP1 (“Sustainability”; low challenges to mitigation and adaptation), 

SSP2 (“Middle of the Road”; middle challenges to mitigation and adaptation), SSP3 (“Regional 
Rivalry”; high challenges to mitigation and adaptation), SSP4 (“Inequality”; low challenges to 
mitigation, high challenges to adaptation), and SSP5 (“Fossil-fueled Development”; high 
challenges to mitigation, low challenges to adaptation). Each of the scenarios has an underlying 
SSP narrative, as well as a consistent quantification of demographic, urbanization, economic 
growth and technology development assumptions. 


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1 To allow IAV researchers to couple alternative socioeconomic scenarios with the climate 

2 scenarios developed using RCPs, SSP-driven scenarios have been constrained using emissions 

3 limitations policies consistent with the underlying SSP story lines to create new scenarios with 

4 climate forcing that matches RCP values, but a range of five alternative socioeconomic 

5 underpinnings. Only SSP5 produces a reference scenario that matches RCP8.5; the other SSPs 

6 have no-climate-policy reference scenarios with climate forcing below 8.5 W/m 2 . Similarly, the 

7 nature of SSP3 makes it impossible for that scenario to produce a climate forcing as low as 2.6 

8 W/m 2 . While new research is under way to explore scenarios that limit climate forcing to 2.0 

9 W/m 2 , neither the RCPs nor the SSPs have produced scenarios in that range. 

10 4.2.3. Global Mean Temperature Scenarios and Pattern Scaling 

1 1 Approaches 

12 RCP scenarios and their associated SSPs provide the input for the global climate model 

13 simulations described in section 4.3 below. The output from these simulations is typically 

14 summarized over a range of future climatological time periods (for example, temperature change 

15 in 2040-2079 or 2070-2099 relative to 1980-2009). The time-slice approach has the advantage 

16 of developing projections for a given time horizon. It has the disadvantage, however, of 

17 including a broad range of uncertainty regarding what may occur over a given time frame, due to 

18 both scenario uncertainty and climate sensitivity. This uncertainty increases, the further out in 

19 time the projections go. A scenario-based approach is also increasingly disconnected with the 

20 framing of many climate targets, including the Paris Agreement, that are expressed in terms of 

21 global mean temperature rather than a given scenario, pathway, or time frame. This is one reason 

22 why the Paris Agreement requested that the IPCC provide a special report on the impacts of a 

23 1.5°C(2.7°F) world. 

24 Global mean temperature (GMT) scenarios provide a way to connect model-based projections to 

25 climate targets, using pre-existing RCP or SRES-based climate model simulations. Traditional 

26 RCP or SRES-based simulations can be transfonned into GMT scenarios by calculating the 

27 projected changes and resulting impacts that would occur under a transient warming of 1°, 2°, or 

28 3°C (1.8°, 3.6°, or 5.4°F) or more. The climatological time slice in each individual model 

29 simulation that corresponds to a given increase in global mean temperature can then be extracted. 

30 This increase can be defined relative to the desired baseline such as preindustrial, for example, or 

31 a more recent time period such as 1976-2005 (Figure 4.2). 

32 Many physical changes and impacts have been shown to scale with GMT, including shifts in 

33 average precipitation, extreme heat, runoff, drought risk, wildfire, temperature-related crop yield 

34 changes, and even risk of coral bleaching (e.g., NRC 2011; Collins et al. 2013; Frieler et al. 

35 2013; Swain and Hayhoe 2015) and this approach has been found to reduce the multimodel 

36 spread of future projections (Herger et al. 2015; Swain and Hayhoe 2015). By quantifying 

37 projected changes for a given amount of warming, regardless of when it may be reached, this 

38 approach de-emphasizes the uncertainty due to both scenarios and climate sensitivity. Instead, 


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GMT scenarios highlight other aspects of scientific uncertainty regarding the response of the 
Earth’s climate system (which can be large, particularly at the regional scale) to human-induced 
change when a given global warming threshold or target is achieved. GMT scenarios are less 
useful for impacts such as species migration, however, that are more dependent on the rate than 
the magnitude of change. 

Pattern scaling techniques (Mitchell 2003) are based on a similar assumption, namely that large- 
scale patterns of regional change will scale with the amount of forcing. These techniques can be 
used to quantify regional change for scenarios that are not readily available in preexisting 
databases of global climate model simulations (as described in section 4.3.1 below), including 
changes in both mean and extremes (e.g., Fix et al. 2016). A comprehensive assessment both 
confirms and constrains the validity of applying pattern scaling to quantify climate response to a 
range of forcings (Tebaldi and Arblaster 2014). As the world moves towards quantifiable climate 
targets, it is expected that these pattern scaling frames or GMT scenarios will become more 
commonly used. 

[INSERT FIGURE 4.2 HERE: 

Figure 4.2: Global mean surface temperature anomalies (°C) relative to 1976-2005 for four RCP 



adapted from Swain and Hayhoe 2015)] 


4 . 2 . 4 . Cumulative Carbon Emissions 

The SRES, RCP, and global mean temperature scenarios described above all contain a 
component of time: how much will climate change, and by when? Ultimately, however, the 
magnitude of human- induced climate change depends less on the year-to-year emissions than it 
does on the net amount of carbon, or cumulative carbon, produced. To date, human activities, 
including burning fossil fuels and deforestation, have emitted more than 600 gigatons of carbon 
(GtC) into the atmosphere since preindustrial times. Unless substantial amounts are removed 
from the atmosphere via carbon sequestration, this amount has already committed the world to at 
least an additional 0.3°C (0.5°F) of wanning over this century, relative to today. 

In order to meet the ambitious 1.5°C (2.7°F) target in the Paris Agreement, only 150 GtC more 
of carbon can be emitted globally. To meet the higher 2°C (3.6°F) target, approximately 400 GtC 
more can be emitted. At current emission rates of just under 10 GtC per year, that would permit 
just 15 years for the lower target and around 40 more years of carbon emissions under the higher 
target. Under the RCP4.5 pathway, cumulative emissions totaling 1000 GtC, consistent with a 
2°C (3.6°F) target, would likely be reached between 205 1 and 2065, while under the RCP8.5 
pathway, this level would likely be reached between 2043 and 2050. For the lower 1.5°C (2.7°F) 
target, the cumulative carbon limit of 750 GtC would likely be reached following the RCP4.5 


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1 pathway sometime between 2028 and 2041, and, following the RCP8.5 pathway, between 2026 

2 and 2036. When non-C02 greenhouse gases such as methane and nitrous oxide (whose warming 

3 potentials differ over time, relative to CO 2 ) are included, exactly when a given temperature 

4 threshold would be exceeded becomes even more uncertain. 

5 The cumulative carbon emissions that would allow the world to meet a given global temperature 

6 target can also be compared to known fossil fuel reserves to calculate how much of their carbon 

7 would have to “stay in the ground” to meet these targets, in the absence of widespread carbon 

8 capture and storage (see Ch. 14). It is estimated that to meet the 2°C (3.6°F) target, two thirds of 

9 known global fossil fuel reserves would need to remain in the ground (McGlade and Ekins 

10 2015). Accounting for the differing carbon content of various types of fuels, in order to meet the 

1 1 2°C target one third of oil reserves, half of gas reserves, and over 80% of coal reserves would 

12 need to remain unused, as well as any new unconventional, undeveloped, or undiscovered 

13 resources (McGlade and Ekins 2015). 

14 4 . 2 . 5 . Paleoclimate Analogues for Long-Term Equilibrium Change 

15 Most CMIP5 simulations project transient changes in climate through 2100; a few simulations 

16 extend to 2200, 2300 or beyond. The long-term impact of human activities on the carbon cycle 

17 and the Earth’s climate, however, can only be assessed by considering changes that occur over 

18 multiple centuries and even millennia, after net human emissions have reached zero, atmospheric 

19 carbon dioxide levels have stabilized, and the carbon cycle has re-balanced (NRC 2011). 

20 In the past, there have been several extended periods of “hothouse” climates where carbon 

21 dioxide concentrations and/or global mean temperatures were similar to preindustrial, current, or 

22 plausible future levels. These periods are sometimes referenced as analogues, albeit imperfect 

23 and incomplete, of future climate (e.g., Crowley 1990). 

24 

25 The last interglacial period, approximately 125,000 years ago, is known as the Eemian. During 

26 that time, CO 2 levels were similar to preindustrial, around 280 ppm (Schneider et al. 2013). 

27 Global mean temperature was approximately 1° to 2°C (1.8° to 3.6°F) higher than preindustrial 

28 levels (Lunt et al. 2012; Otto-Bleisner et al. 2013), the poles were significantly warmer (NEEM 

29 2013; Jouzel et al. 2007), and sea level was 6 to 9 meters (20 to 30 feet) higher than today (Fig. 

30 4.3; Kopp et al. 2009). During the Pliocene, approximately 3 million years ago, long-tenn CO 2 

3 1 levels were similar to today’s, around 400 ppm (Seki et al. 2010) - although those concentrations 

32 were sustained over long periods of time, whereas ours are increasing rapidly. Global mean 

33 temperature in the Pliocene was approximately 2° to 3.5°C (3.6° to 6.3°F) above preindustrial, 

34 and sea level was somewhere between 20 ± 10 meters (66 ± 33 feet) higher than today (Fig. 4.3; 

35 Haywood et al. 2013; Dutton et al. 2015; Miller et al. 2012). 

36 

37 Under the higher RCP8.5 scenario, CO 2 concentrations are projected to exceed 900 ppm before 

38 2100. During the Eocene, 35 to 55 million years ago, CO 2 levels were between 680 and 1260 


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ppm, or two and a half to four and a half times above preindustrial levels (Jagniecki et al. 2015). 
Using Eocene conditions as an analogue, this suggests that if the CO 2 concentrations projected to 
occur under the RCP8.5 scenario by 2100 were sustained over long periods of time, they would 
result in global temperatures approximately 5° to 8°C (9° to 14°F) above preindustrial levels 
(Royer 2014). During the Eocene, there were no permanent land-based ice sheets; Antarctic 
glaciation did not begin until approximately 34 million years ago (Pagani et al. 2011). 

Calibrating sea level rise models against these and other past climate conditions suggests that, 
under the RCP8.5 scenario, Antarctica could contribute 1 meter of sea level rise by 2100 and 15 
meters by 2500 (DeConto and Pollard 2016). If atmospheric CO 2 were sustained at levels 
approximately two to three times above preindustrial for tens of thousands of years, it’s 
estimated that Greenland and Antarctic ice sheets could melt entirely (Gasson et al. 2014), 
resulting in approximately 65 meters (215 feet) of sea level rise relative to present-day (Vaughn 
et al. 2013). 

An analog for the rapid pace of change occurring today is the relatively abrupt warming of 5° to 
8°C (9° to 14°F) that occurred during the Paleocene-Eocene Thermal Maximum (PETM), 
approximately 55-56 million years ago (Bowen et al. 2015; Kirtland Turner et al. 2014; Penman 
et al. 2014; Crowley et al. 1990). However, new analyses reveal that this carbon was released 
over some 4000 years or so, and the rate of maximum sustained carbon release during that period 
was less than 1.1 GtC per year (Zeebe et al. 2016). In comparison, industrial era emissions have 
occurred over a few centuries, at rates now approaching 10 GtC per year. This suggests that there 
is no real past analogue any time in the last 66 million years that could help to constrain 
projections of future climate (Zeebe et al. 2016; Crowley et al. 1990). 

[INSERT FIGURE 4.3 HERE: f 

Figure 4.3: Putting present-day global mean temperature, CO 2 concentrations, and sea level into 
context, this figure summarizes what is known about the range in peak global mean temperature, 
atmospheric CO 2 , maximum global mean sea level (GMSL), and source(s) of meltwater over 
three periods in the past with CO 2 levels similar to pre-industrial levels (around 270 ppm) or 
today (around 400 ppm). Light blue shading indicates uncertainty of GMSL maximum. Red pie 
charts over Greenland and Antarctica denote fraction, not location, of ice retreat. (Figure source: 
Dutton et al. 2015)] 

4.3. Modeling Tools 

4.3.1. Global Climate Models 

Climate scientists use a wide range of observational and computational tools to understand the 
complexity of the Earth’s climate system and to study how that system responds to external 
forces, including human activities. Computational tools include models that simulate different 
components of the climate system, including the atmosphere, ocean, land, and sea ice (see Ch. 2: 
Scientific Basis). The most sophisticated computational tools used by climate scientists are 


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global climate models, or GCMs (previously referred to as “general circulation models” when 
they included only the physics needed to simulate the general circulation of the atmosphere and 
oceans). Models that include an interactive carbon cycle and/or biogeochemistry component are 
sometimes also referred to as Earth System Models (ESMs). 

Global climate models are mathematical models originally built on fundamental equations of 
physics that include the conservation of energy, mass, and momentum, and how these are 
exchanged among different components of the climate system. Using these fundamental 
relationships, the models generate many important features that are evident in the Earth’s climate 
system: the jet stream that circles the globe 30,000 feet above the Earth’s surface; the Gulf 
Stream and other ocean currents that transport heat from the tropics to the poles; and even 
hurricanes in the Atlantic Ocean and typhoons in the Pacific Ocean when the models are run at a 
fine enough spatial resolution. 

[INSERT FIGURE 4.4 HERE: 

Figure 4.4: As climate modeling has evolved over the last 120 years, increasing amounts of 
physical science have been incorporated into the models. This figure shows the evolution from 
simple energy balance models through atmosphere-ocean general circulation models to today’s 
earth system models.] 

In addition to expanding the number of processes in the models and improving the treatment of 
existing processes, the average horizontal spatial resolution of GCMs has increased over time, as 
computers become more powerful, and with each successive version of the World Climate 
Research Programme’s (WCRP’s) Coupled Model Intercomparison Project (CMIP). CMIP5 
provides output from over 50 GCMs with spatial resolutions ranging from about 50 to 300 km 
(30 to 200 miles) per horizontal size, and variable vertical resolution on the order of hundreds of 
meters in the troposphere or lower atmosphere. Versions 3 and 5 are currently available, and 
Version 6 is underway [Note: we will update CMIP6 progress in subsequent drafts]. These 
simulations provide output from a large ensemble of different climate models and future 
scenarios to quantify future climate change at global, continental, and broad regional scales. 

GCMs are constantly being expanded to include more physics, chemistry, and, increasingly, even 
the biology and biogeochemistry at work in the climate system (Figure 4.4). However, these 
models build on previous generations and are not independent from each other. Many share both 
ideas and model components or code, complicating the interpretation of multi-model ensembles 
that often are assumed to be independent (Knutti et al. 2013; Sanderson et al. 2015). This is one 
of the key pieces of information going into the weighting approach used in this report (see 
Weighting Appendix). And even with new experimental high-resolution simulations at 25 km 
(15 miles) per horizontal grid cell, there are still important fine-scale processes occurring at 
regional to local scales that GCMs are unable to simulate. 


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1 To translate global projections into the higher-resolution infonnation often required for impact 

2 assessment, climate impact studies often use the statistical or dynamical downscaling methods 

3 discussed above. Regional climate models can directly simulate the response of regional climate 

4 processes to global change, while statistical models can remove biases in simulations relative to 

5 observations. While some new approaches are combining dynamical and statistical methods into 

6 a hybrid framework, most assessments still tend to rely on one or the other type of downscaling, 

7 where the choice is based on the needs of the assessment. 

8 4.3.2. Regional Climate Models 

9 Dynamical downscaling models are often referred to as regional climate models (RCMs), since 

10 they include many of the same physical processes that make up a global climate model, but 

1 1 simulate these processes at higher resolution over smaller rectangular regions, such as the 

12 western or eastern United States. Regional climate modeling can improve understanding of 

13 regional climate change by modeling areas with complex terrain, such as coastlines or 

14 mountains. They can also incorporate changes in land use, land cover, or hydrology into local 

15 climate at spatial scales relevant to planning and decision-making at the regional level. 

16 RCMs are computationally intensive because of the higher resolution, but provide a broad range 

17 of output variables that resolve regional climate features important for assessing climate impacts. 

18 The size of individual grid cells can be as fine as 1 to 2 km (0.6 to 1.2 miles) per horizontal side 

19 in some studies, but more commonly range from about 10 to 50 km (6 to 30 miles). Despite the 

20 differences in resolution, RCMs are still subject to many of the same types of uncertainty as 

21 GCMs, such as not fully resolving physical processes that occur at even smaller scales than the 

22 model is able to resolve. One additional source of uncertainty unique to RCMs arises from the 

23 fact that at their boundaries RCMs require output from GCMs to provide large-scale circulation 

24 such as winds, temperature, and moisture. 

25 The North America Coordinated Regional Climate Downscaling Experiment (CORDEX; Note: 

26 in progress, will need to be updated in subsequent drafts) is currently generating a set of high- 

27 resolution RCM simulations for North America at spatial resolutions ranging from 10 to 50 km 

28 (6 to 30 miles) with 3-hour outputs for more than 60 different surface and upper-air variables. 

29 Currently-available simulations from the North American Regional Climate Change Assessment 

30 Program are useful for examining certain impacts over North America but, as they are based on 

3 1 simulations from four CMIP3 GCMS for a single mid-high SRES scenario, do not encompass 

32 the full range of uncertainty in future projections due to both human activities and climate 

33 sensitivity, as represented by the range of CMIP5 GCMs and RCP scenarios. 

34 If the study is a sensitivity analysis, where using one or two future simulations is not a limitation, 

35 or if it requires many climate variables as input, then regional climate modeling may be more 

36 appropriate than statistical modeling. Kotamarthi et al. (2016) provides a full discussion of the 


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issues surrounding selecting and applying dynamical and statistical downscaling methods to 
assess climate impacts. 

[INSERT FIGURE 4.5 HERE: 

Figure 4.5: Global climate models typically operate at coarser horizontal spatial scales, while 


regional climate models have much finer resolutions. This figure compares annual average 


precipitation for the historical period 1979-2008 using (a) a resolution of 25 km or 15 miles with 

(b) a resolution of 250 km or 150 miles, to illustrate the importance of spatial scale in resolving 


key topographical features, particularly along the coasts and in mountainous areas. In this case, 

both simulations are by the GFDL HIRAM model, an experimental high-resolution model. 



(Figure source: adapted from Dixon et al. 2016; © American Meteorological Society, used 

with 



permission)] 


4.3.3. Empirical Statistical Downscaling Models 

Empirical statistical downscaling models (ESDMs) combine GCM output with historical 
observations to translate large-scale predictors or patterns into high-resolution projections at the 
scale of observations. The observations used in an ESDM can range from individual weather 
stations to gridded datasets. As output, they can generate a range of products, from large grids to 
analyses optimized for a specific location, variable, or decision-context. The statistical 
techniques are even more varied, from simple difference or delta approaches (subtracting 
historical simulated values from future values, and adding the resulting delta to historical 
observations, as used in the First National Climate Assessment) to complex clustering and neural 
network techniques that can rival dynamical downscaling in their demand for computational 
resources (see review by Kotamarthi et al. 2016). 

Statistical models are generally flexible and less computationally demanding than RCMs. A 
number of databases provide statistically downscaled projections for a continuous period from 
1960 to 2100 using many global models and a range of higher and lower future scenarios. 
ESDMs are also effective at removing biases in historical simulated values, leading to a good 
match between the average (multidecadal) statistics of observed and statistically downscaled 
climate at the spatial scale and over the historical period of the observational data used to train 
the statistical model. With the exception of methods that simultaneously downscale multiple 
variables, however, bias correction will remove the physical interdependence between variables. 

ESDMs are also limited in that they require observational data as input; the longer and more 
complete the record, the greater the confidence that the ESDM is being trained on a 
representative sample of climatic conditions for that location. Application of ESDMs to remote 
locations with sparse temporal and/or spatial records is challenging though in many cases 
reanalysis (Brands et al. 2012) or even monthly satellite data (Thrasher et al. 2013) can be used 
in lieu of in situ observations. Lack of data availability can also limit their use in applications 
that require more variables than temperature and precipitation. 


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1 Finally, statistical models are based on the key assumption that the relationship between large- 

2 scale weather systems and local climate or the spatial pattern of surface climate will remain 

3 stationary over the time horizon of the projections. This assumption may not hold if climate 

4 change alters local feedback processes that affect these relationships; initial analyses have 

5 demonstrated that the assumption of stationarity can vary significantly by ESDM method, by 

6 quantile, and by the time scale (daily or monthly) of the GCM input (Dixon et al. 2016). 

7 ESDMs are best suited for analyses that require a broad range of future projections of standard, 

8 near-surface variables such as temperature and precipitation, at the scale of observations that 

9 may already be used for planning purposes. If the study needs to resolve the full range of 

10 projected changes under multiple models and scenarios or is more constrained by practical 

1 1 resources, then statistical downscaling may be more appropriate than dynamical downscaling. 

12 However, even within statistical downscaling, selecting an appropriate method for any given 

13 study depends on the questions being asked; these issues are discussed in greater detail by 

14 Kotamarthi et al. (2016). 

15 4.3.4. Averaging, Weighting, and Selection of Global Models 

16 Individual climate model simulations using the same inputs can differ from each other over 

17 several years to several decades. These differences are the result of normal, natural variability as 

18 well as the different ways models characterize various small-scale processes. Although decadal 

19 predictability is an active research area, the timing of natural variations is largely unpredictable 

20 beyond several seasons. For this reason, multimodel simulations are generally averaged (as the 

21 last stage in any analysis before preparing, for example, figures showing projected changes in 

22 annual or seasonal temperature or precipitation; see Ch. 6 and 7) to remove the effects of 

23 randomly occurring natural variations from long-term trends and make it easier to discern the 

24 impact of external drivers, both human and natural, on the Earth’s climate. The effect of 

25 averaging on the systematic errors depends on the extent to which models have similar errors or 

26 offsetting errors. For that reason, on time series plots, we also show a range of outcomes across 

27 GCMs, quantify the risks inherent to a given scenario. 

28 Previous assessments have used a simple average to calculate the multimodel ensemble. Such 

29 approach implicitly assumes each climate model is independent from the others and of equal 

30 ability. Neither of these assumptions, however, are completely valid. As noted previously, some 

3 1 models share many components with other models in the CMIP5 archive, whereas others have 

32 been developed largely in isolation (Knutti et al. 2013; Sanderson et al. 2015). Also, some 

33 models are more successful than others: at replicating observed climate and trends over the past 

34 century; at simulating the large-scale dynamical features responsible for creating or affecting the 

35 average climate conditions over a certain region, such as the Arctic or the Caribbean (e.g., Wang 

36 et al. 2007, 2014; Ryu and Hayhoe 2014); or at simulating past climates with very different states 

37 than present day (Braconnot et al. 2012). Evaluation of models’ success often depends on the 

38 variable or metric being considered in the analysis, with some models performing better than 


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1 others for certain regions or variables. However, all future simulations agree that both global and 

2 regional temperatures will increase over this century in response to increasing emissions of 

3 greenhouse gases from human activities. 

4 For the first time in an official U.S. Global Change Research Program report, this assessment 

5 uses model weighting to refine future climate change projections (see Appendix B: Model 

6 Weighting). The weighting approach takes into account the interdependence of individual 

7 climate models and their relative abilities in simulating North American climate. Understanding 

8 of model history, together with the fingerprints of particular model biases, has been used to 

9 identify model pairs that are not independent. In this report, model independence and selected 

10 global and North American model quality metrics are considered in order to detennine the 

1 1 weighting parameters (Sanderson et al. in prep). 

12 Sensitivity studies in the implementation of the weighting scheme show that global-scale 

13 temperature response is not significantly constrained by the weighting strategy, although there 

14 are small regional differences in significance. The choice of metric used to evaluate models has 

15 very little effect on the independence weighting, and some moderate influence on the skill 

16 weighting if only a small number of variables are used to assess model quality. Because a large 

17 number of variables are combined to produce a comprehensive “skill metric,” the metric is not 

18 highly sensitive to any single variable. 

19 4.4. Uncertainty in Future Projections 

20 The magnitude of future climate change depends on human choices (see Section 4.2), natural 

21 variability, and scientific uncertainty (Hawkins and Sutton 2009, 2011; Deser et al. 2012). 

22 Scientific uncertainty in turn encompasses multiple factors. The first is parametric uncertainty — 

23 the ability of GCMs to simulate processes that occur on spatial or temporal scales smaller than 

24 they can resolve. The second is structural uncertainty — whether GCMs include and accurately 

25 represent all the important physical processes occurring on scales they can resolve. Structural 

26 uncertainty can arise because a process is not yet recognized — such as “tipping points” or 

27 mechanisms of abrupt change, as discussed in Ch. 15, Potential Surprises — or because it is known 

28 but is not yet understood well enough to be modeled accurately — such as dynamical mechanisms 

29 that are important to melting ice sheets. The third is climate sensitivity — a measure of the 

30 response of the planet to increasing levels of CO 2 , formally defined as the equilibrium 

3 1 temperature change resulting from a doubling of CO 2 levels in the atmosphere relative to 

32 preindustrial levels. Various lines of evidence constrain the likely value of climate sensitivity. 

33 These include historical warming (in the instrumental record, as well as events in the paleo- 

34 climate record, such as the transition from the Last Glacial Maximum to today), and combining 

35 analysis of aspects of present-day climate with physical modeling of the climate system to 

36 constrain possible feedbacks such as how clouds might change in a wanner world (Knutti and 

37 Hegerl 2008). Combining this evidence, climate sensitivity is likely to lie between 2°C and 4.5°C 

38 (3.6°F and 8.1°F; IPCC 2013b). 


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Which of these sources of uncertainty — human, natural, and scientific — is most important 
depends on the time frame and the variable considered. For temperature, it is clear that 
increasing greenhouse gas emissions from human activities will drive increases in global and 
most regional temperatures and that these rising temperatures will increase with the magnitude of 
future emissions, particularly past mid-century (Hawkins and Sutton 2009). Uncertainty in 
projected temperature change is generally smaller than uncertainty in projected changes in 
precipitation or other aspects of climate. For precipitation, the processes that fonn precipitation 
happen below the scale of the models, requiring a significant amount of parameterization. For 
that reason, scientific uncertainty tends to dominate in precipitation projections throughout the 
entire century (Hawkins and Sutton 2011). 

Over the next few decades, the greater part of the range or uncertainty in projected global and 
regional change is the result of a combination of natural variability (mostly related to uncertainty 
in specifying the initial conditions of the state of the ocean) and scientific limitations in our 
ability to model and understand the Earth’s climate system (Figure 4.6). Differences in forcing 
scenarios, shown in orange in Figure 4.6, represent the scenarios, or human uncertainty. Over the 
short term, these differences are relatively small. As time progresses, however, differences in 
emissions become larger and the delayed ocean response to these differences begins to be 
realized. By about 2030, the human source of uncertainty becomes increasingly important in 
detennining the magnitude and patterns of future change. Even though natural variability will 
continue to occur, most of the difference between present and future climates will be determined 
by choices that society makes today and over the next few decades. The further out in time we 
look, the greater the influence of these differences in human choices are on the differences in 
magnitude of future change. 

[INSERT FIGURE 4.6 HERE: 

Figure 4.6: The fraction of total variance in decadal mean surface air temperature predictions 
explained by the three components of total uncertainty is shown for (a) Alaska, (b) Hawai’i, and 
(c) the lower 48 states. Orange regions represent human or scenario uncertainty, blue regions 
represent model uncertainty, and green regions represent the internal variability component. As 
the size of the region is reduced, the relative importance of internal variability increases. In 
interpreting this figure, it is important to remember that it shows the fractional sources of 
uncertainty. Total uncertainty increases as time progresses. (Figure source: adapted from 
Hawkins and Sutton 2009)] 


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1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 Merely maintaining present-day levels of greenhouse (heat-trapping) gases in the atmosphere 

4 would commit the world to at least an additional 0.3°C (0.5°F) of warming over this century 

5 relative to today (high confidence). Projections over the next three decades differ modestly, 

6 primarily due to uncertainties in natural sources of variability. Past mid-century, the amount of 

7 climate change depends primarily on future emissions and the sensitivity of the climate system to 

8 those emissions. 

9 Description of evidence base 

10 The basic physics underlying the impact of human emissions on global climate, and the role of 

1 1 climate sensitivity in moderating the impact of those emissions on global temperature, has been 

12 documented since the 1800s in a series of peer-reviewed journal articles that is summarized in a 

13 collection titled, “The Wanning Papers: The Scientific Foundation for the Climate Change 

14 Forecast” (Archer and Pierrehumbert 2011). 

15 IPCC AR5 WG1 SPM states “Total radiative forcing is positive, and has led to an uptake of 

16 energy by the climate system. The largest contribution to total radiative forcing is caused by the 

17 increase in the atmospheric concentration of CO 2 since 1750” (C, page 13) and “Observational 

18 and model studies of temperature change, climate feedbacks and changes in the Earth’s energy 

19 budget together provide confidence in the magnitude of global wanning in response to past and 

20 future forcing.” (IPCC 2013b, D.2, page 16) 

21 The estimate of committed wanning at constant atmospheric concentrations is based on IPCC 

22 AR5 WG 1 , Collins et al. 20 1 3 . 

23 Analysis of the sources of uncertainty in near-term versus long-term projections have been made 

24 by Hawkins & Sutton (2009, 2011) and Deser et al. (2012). 

25 Major uncertainties 

26 In the statement, virtually none. In future emissions and climate sensitivity, there are significant 

27 uncertainties as reflected by the focus of this Key Message. 

28 

29 Assessment of confidence based on evidence and agreement, including short description of 

30 nature of evidence and level of agreement 

31 X Certain (100%) 

32 □ Very High 

33 X High 

34 □ Medium 

35 DLow 

36 The first statement regarding additional wanning has high confidence in the amount of warming; 

37 the second is virtually certain, as understanding of the radiative properties of greenhouse gases 


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1 and the existence of both positive and negative feedbacks in the climate system is basic physics, 

2 dating to the 19th century. 

3 Summary sentence or paragraph that integrates the above information 

4 The key finding is based on basic physics that has been well established for decades to centuries 

5 and is referenced in every IPCC report from FAR to AR5. 

6 

7 Key Finding 2 

8 Atmospheric carbon dioxide (CO 2 ) levels have now passed 400 ppm, a concentration last seen 

9 about 3 million years ago, when average temperature and sea level were significantly higher than 

10 today. Continued growth in CO 2 emissions over this century and beyond would lead to 

1 1 concentrations not experienced in tens to hundreds of millions of years. The rapid present-day 

12 emissions rate of nearly 10 GtC per year, however, suggests that there is no precise past climate 

13 analogue for this century any time in at least the last 66 million years. (. Medium confidence) 

14 Description of evidence base 

15 The Key Finding is based on a large body of research including Crowley (1990), Schneider et al. 

16 (2013), Lunt et al. (2012), Otto-Bleisner et al. (2013), NEEM (2013), Jouzel et al. (2007), Dutton 

17 et al. (2015), Seki et al. (2010), Haywood et al. (2013), Miller et al. (2012), Royer (2014), 

18 Bowen et al. (2015), Kirtland Turner et al. (2014), Penman et al. (2014), Zeebe et al. (2016), and 

19 summarized in NRC (2011) and Masson-Dehnotte et al. (2013). 

20 Major uncertainties 

21 The largest uncertainty is the measurement of past sea level, given the contributions of not only 

22 changes in land ice mass, but also in solid earth, mantle, isostatic adjustments, etc. that occur on 

23 timescales of millions of years. This uncertainty increases the further back in time we go; 

24 however, the signal (and forcing) size is also much greater. There are also associated 

25 uncertainties in precise quantification of past global mean temperature and carbon dioxide levels. 

26 There is uncertainty in the age models used to determine rates of change and coincidence of 

27 response at shorter, sub-millennial timescales. 

28 Assessment of confidence based on evidence and agreement, including short description of 

29 nature of evidence and level of agreement 

30 □ Very High 

31 DHigh 

32 X Medium 

33 DLow 

34 Medium confidence in the likelihood statement that past global mean temperature and sea level 

35 rise were higher with similar or higher CO 2 concentrations is based on Masson-Dehnotte et al. 

36 (2013) in IPCC AR5. Medium confidence that no precise analog exists in 66 million years is 


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1 based on Zeebe et al. (2016) as well as the larger body of literature summarized in Masson- 

2 Delmotte et al. (2013). 

3 Summary sentence or paragraph that integrates the above information 

4 The key finding is based on a vast body of literature that summarizes the results of observations, 

5 paleoclimate analyses, and paleoclimate modeling over the past 50 years and more. 

6 

7 Key Finding 3 

8 The observed acceleration in carbon emissions over the past 15-20 years is consistent with 

9 higher future scenarios ( very high confidence). Since 2014, growth rates have slowed as 

10 economic growth begins to uncouple from carbon emissions ( medium confidence) but not yet at a 

1 1 rate that, were it to continue, would limit atmospheric temperature increase to the 2009 

12 Copenhagen goal of 2°C (3.6°F), let alone the 1.5°C (2.7°F) target of the 2015 Paris Agreement 

13 ( high confidence). 

14 Description of Evidence Base 

15 Observed emissions for 2014 and 2015 and estimated emissions for 2016 suggest a decrease in 

16 the growth rate and possibly even emissions of carbon; this shift is attributed primarily to 

17 decreased coal use in China although with significant uncertainty as noted in the references in 

1 8 the text. 

19 All credible climate models assessed in Chapter 9 of the IPCC WG1 AR5 (IPCC 2013a) from the 

20 simplest to the most complex respond with elevated global mean temperature, the simplest 

21 indicator of climate change, when greenhouse gases increase. It follows then that an emissions 

22 pathway that tracks or exceeds RCP8.5 would lead to larger amounts of climate change. 

23 The evidence that actual emission rates track or exceed the RCP8.5 scenario are as follows. The 

24 actual emission of CO 2 from fossil fuel consumption and concrete manufacture over the period 

25 2005-2014 is 90.1 1 Pg (Le Quere et al. 2015) The RCP8.5 emissions over the same period 

26 assuming linear trends between years in the specification is 89.01 Pg. 

27 Actual emissions: http://www.globalcarbonproject.org/carbonbudget/15/data.htm and Le Quere 

28 etal. (2015). 

29 RCP8.5 emissions 

30 http://tntcat.iiasa.ac. at:8787/RcpDb/dsd?Action=htmlpage&page=compare 

3 1 The actual numbers (red is estimated). 

32 


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RCP8.5 

Actual 

difference 

2005 

7.971 

8.076 

0.105 

2006 

8.162 

8.363 

0.201 

2007 

8.353 

8.532 

0.179 

2008 

8.544 

8.74 

0.196 

2009 

8.735 

8.7 

-0.035 

2010 

8.9256 

9.14 

0.2144 

2011 

9.18716 

9.449 

0.26184 

2012 

9.44832 

9.575025506 

0.126705506 

2013 

9.70948 

9.735033958 

0.025553958 

2014 

9.97064 

9.795211382 

-0.175428618 


89.0062 

90.10527085 

1.099070845 


Major Uncertainties 

None 

Assessment of confidence based on evidence and agreement, including short description of 
nature of evidence and level of agreement 

□ Certain (100%) 

X Very High 

X High 
X Medium 

□ Low 

Very high confidence in increasing emissions over the last 20 years and high confidence in the 
fact that recent emission trends will not be sufficient to avoid 2°C. Medium confidence in recent 
findings that the growth rate is slowing and/or emissions are plateauing soon. Climate change 
scales with the amount of anthropogenic greenhouse gas in the atmosphere. If emissions exceed 
RCP8.5, the likely range of changes temperatures and climate variables will be larger than 
projected. 

Summary sentence or paragraph that integrates the above information 

The key finding is based on basic physics relating emissions to concentrations, radiative forcing, 
and resulting change in global mean temperature as well as on IEA data on national emissions as 
reported in the peer-reviewed literature. 


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1 Key Finding 4 

2 Combining output from global climate models and dynamical and statistical downscaling models 

3 using advanced averaging, weighting, and pattern scaling approaches can result in more relevant 

4 and robust future projections. These techniques also allow the scientific community to provide 

5 better guidance on the use of climate projections for quantifying regional-scale impacts ( medium 

6 to high confidence). 

7 Description of evidence base 

8 The contribution of weighting and pattern scaling to improving the robustness of multimodel 

9 ensemble projections is described and quantified by a large body of literature as summarized in 

10 the text. The state of the art of dynamical and statistical downscaling and the scientific 

1 1 community’s ability to provide guidance regarding the application of climate projections to 

12 regional impact assessments is summarized in Kotamarthi et al. (2016). This peer-reviewed DOD 

13 SERDP report documents new advances in testing and evaluating empirical statistical 

14 downscaling methods. This is the best available reference at this time, as downscaling receives 

15 only cursory mention in IPCC AR5 and — despite proposals for a report on this topic — has yet to 

16 be the focus of an NAS report. 

17 Major uncertainties 

18 Regional climate models are subject to the same structural and parametric uncertainties as global 

19 models, as well as the uncertainty due to incorporating boundary conditions. The primary source 

20 of error in application of empirical statistical downscaling methods is inappropriate application, 

2 1 followed by stationarity. 

22 Assessment of confidence based on evidence and agreement, including short description of 

23 nature of evidence and level of agreement 

24 □ Very High 

25 DHigh 

26 X Medium 

27 DLow 

28 Advanced weighting techniques have significantly improved over previous Bayesian approaches; 

29 confidence in their ability to improve the robustness of multi-model ensembles, while currently 

30 rated as medium, is likely to grow in coming years. Downscaling has evolved significantly over 

3 1 the last decade and is now broadly viewed as a robust source for high-resolution climate 

32 projections that can be used as input to regional impact assessments. 

33 Summary sentence or paragraph that integrates the above information 

34 Scientific understanding of climate projections, downscaling, multi-model ensembles, and 

35 weighting has evolved significantly over the last decades to the extent that appropriate methods 

36 are now broadly viewed as robust sources for climate projections that can be used as input to 

37 regional impact assessments. 

38 


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FIGURES 


SRES Scenarios 
30 



Year 


30 
25 

Q 

<3 20 

j 15 

£ io 
1 5 

O 

0 
-5 

2000 2020 2040 2060 2080 2100 

Year 


RCP Scenarios 





Figure 4.1: The climate projections used in this report are based on the 2010 Representative 
Concentration Pathways (RCP, right). They are largely consistent with scenarios used in 
previous assessments, the 2000 Special Report on Emission Scenarios (SRES, left). This figure 
compares SRES and RCP annual carbon emissions (top), carbon dioxide equivalent levels in the 
atmosphere (middle), and temperature change that would result from the central estimate (lines) 
and the likely range (shaded areas) of climate sensitivity (bottom). (Data from CMIP3 and 
CMIP5). (Figure source: Walsh et al. 2014) 


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1 


2 

3 

4 

5 

6 

7 

8 
9 



u 

o 

(/) 

0 ) 

Id 

3 E 
o 
c 
< 

I— 

1/1 


- 1 


Figure 4.2: Global mean surface temperature anomalies (°C) relative to 1976-2005 for four RCP 
scenarios, 2.6 (green), 4.5 (yellow), 6.0 (orange), and 8.5 (red), calculated in 0.5°C increments. 
Each line represents an individual simulation from the CMIP5 archive; every RCP-based CMIP5 
simulation with annual or monthly temperature outputs available was used here. (Figure source: 
adapted from Swain and Hayhoe 2015) 


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Temperature relative to preindustrial 


~2-3°C 



125,000 400,000 3,000,000 

Years before present 


Figure 4.3: Putting present-day global mean temperature, CO 2 concentrations, and sea level into 
context, this figure summarizes what is known about the range in peak global mean temperature, 
atmospheric CO 2 , maximum global mean sea level (GMSL), and source(s) of meltwater over 
three periods in the past with CO 2 levels similar to pre-industrial levels (around 270 ppm) or 
today (around 400 ppm). Light blue shading indicates uncertainty of GMSL maximum. Red pie 
charts over Greenland and Antarctica denote fraction, not location, of ice retreat. (Figure source: 
Dutton et al. 2015) 


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1 

2 

A climate modeling timeline (when various components became commonly used) 


3 

4 

5 

6 

7 

8 
9 

10 


RADIATIVE 

TRANSFER 


NON-LINEAR HYDROLOGICAL 

FLUID DYNAMICS CYCLE 


SEA ICE AND AEROSOLS AND BIOGEOCHEMICAL 

LAND SURFACE CHEMISTRY CYCLES AND CARBON 


( 

\ 

\ 

\ 


\ 


t 

L 


i 

—J 


1890s 

ENERGY BALANCE 
MODELS 



1960s - 1980s 


ATMOSPHERE-OCEAN GENERAL CIRCULATION MODELS 


1990s 2000s 2010s 

EARTH SYSTEM 


MODELS 


Figure 4.4: As climate modeling has evolved over the last 120 years, increasing amounts of 
physical science have been incorporated into the models. This figure shows the evolution from 
simple energy balance models through atmosphere-ocean general circulation models to today’s 
earth system models. 


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0 400 800 1200 1600 2000 0 400 800 1200 1600 2000 


Figure 4.5: Global climate models typically operate at coarser horizontal spatial scales, while 
regional climate models have much finer resolutions. This figure compares annual average 
precipitation for the historical period 1979-2008 using (a) a resolution of 25 km or 15 miles with 
(b) a resolution of 250 km or 150 miles, to illustrate the importance of spatial scale in resolving 
key topographical features, particularly along the coasts and in mountainous areas. In this case, 
both simulations are by the GFDL HIRAM model, an experimental high-resolution model. 
(Figure source: adapted from Dixon et al. 2016; © American Meteorological Society, used with 
permission) 


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(a) 

100 


90 

g 

80 

0 

o 

c 

CO 

70 

CO 

> 

60 

2 

o 

50 

w 

H 


o 

40 

c 

o 

30 

CO 


LL 

20 


10 


Uncertainty in Alaska Decadal Mean ANN Temperature 


Internal variability 


Future emissions uncertainty 


Model uncertainty 


0 


2020 2040 2060 2080 2100 

Year 


(b) Uncertainty in Hawaii Decadal Mean ANN Temperature 


Internal variability 



100 


90 

£ 

80 

0 

O 

c 

CO 

70 

CO 

> 

60 

2 

50 

f— 


b 

40 

c 

o 

30 

CO 


LL 

20 


10 


Future emissions uncertainty 


Model uncertainty 


2020 


2040 


2060 


2080 


2100 


Year 


Figure 4.6: The fraction of total 
variance in decadal mean surface air 
temperature predictions explained by 
the three components of total 
uncertainty is shown for (a) Alaska, (b) 
Hawai’I, and (c) the lower 48 states 
(bottom). Orange regions represent 
human or scenario uncertainty, blue 
regions represent model uncertainty, 
and green regions represent the internal 
variability component. As the size of 
the region is reduced, the relative 
importance of internal variability 
increases. In interpreting this figure, it 
is important to remember that it shows 
the fractional sources of uncertainty. 
Total uncertainty increases as time 
progresses. (Figure source: adapted 
from Hawkins and Sutton 2009) 


(c) 


100-1 
90 j 

g 80 j 
0 

c 70 1 
® 60 1 

o 


c 

o 

T5 

co 


Uncertainty in USA Decadal Mean ANN Temperature 


Internal variability 


50 1 
40 1 
30 j 

20 j 

lo] 


Future emissions uncertainty 


Model uncertainty 


2020 


2040 


2060 


2080 


2100 


Year 


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Chapter 4 


1 REFERENCES 

2 Archer, D. and R. Pierrehumbert, eds. The Warming Papers: The Scientific Foundation for the 

3 Climate Change Forecast. 2011, Wiley-Blackwell: Oxford, UK. 432. 

4 Bowen, G.J., B J. Maibauer, MJ. Kraus, U. Rohl, T. Westerhold, A. Steimke, P.D. Gingerich, 

5 S .L. Wing, and W.C. Clyde, 2015: Two massive, rapid releases of carbon during the onset of 

6 the Palaeocene-Eocene thermal maximum. Nature Geosci, 8, 44-47. 

7 http ://dx .doi .org / 1 0 . 1 03 8/ngeo23 1 6 

8 Braconnot, P., S.P. Harrison, M. Kageyama, PJ. Bartlein, V. Masson-Delmotte, A. Abe-Ouchi, 

9 B . Otto-Bliesner, and Y. Zhao, 2012: Evaluation of climate models using palaeoclimatic 

10 data. Nature Clim. Change, 2, 417-424. http://dx.doi.org/10.1038/nclimatel456 

1 1 Brands, S., J.M. Gutierrez, S. Herrera, and A.S. Cofino, 2012: On the Use of Reanalysis Data for 

12 Downscaling. Journal of Climate, 25, 2517-2526. http://dx.doi.Org/10.1175/jcli-d-ll-00251.l 

13 Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W J. 

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24 UNFCCC, 2015: Paris Agreement. United Nations Framework Convention on Climate Change, 

25 [Bonn, Germany]. 

26 Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. 

27 Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen, and T. Zhang, 2013: Observations: 

28 Cryosphere. Climate Change 2013: The Physical Science Basis. Contribution of Working 

29 Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

30 Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, 

31 V. Bex, and P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom 

32 and New York, NY, USA, 317-382. http://dx.doi.org/10.1017/CB09781107415324.012 

33 www .climatechange20 1 3 .org 

34 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

35 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 


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1 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

2 Climate Change Impacts in the United States: The Third National Climate Assessment. 

3 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

4 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

5 Wang, C., L. Zhang, S.-K. Lee, L. Wu, and C.R. Mechoso, 2014: A global perspective on 

6 CMIP5 climate model biases. Nature Climate Change, 4, 201-205. 

7 http ://dx .doi .org / 1 0 . 1 03 8/nclimate2 118 

8 Wang, M., J.E. Overland, V. Kattsov, J.E. Walsh, X. Zhang, and T. Pavlova, 2007: Intrinsic 

9 versus Forced Variation in Coupled Climate Model Simulations over the Arctic during the 

10 Twentieth Century. Journal of Climate, 20, 1093-1107. http://dx.doi.org/10T 175/JCLI4043.1 

1 1 Zeebe, R.E., A. Ridgwell, and J.C. Zachos, 2016: Anthropogenic carbon release rate 

12 unprecedented during the past 66 million years. Nature Geoscience, 9, 325-329. 

1 3 http ://dx .doi .org/ 1 0 . 1 03 8/ngeo268 1 


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5. Large-Scale Circulation and Climate Variability 

KEY FINDINGS 

1 . Under increased greenhouse gas concentrations, the tropics are likely to expand with an 
accompanying poleward shift of the subtropical dry zones and midlatitude jets in each 
hemisphere ( medium to high confidence). While it is likely that tropics have expanded 
since 1979 ( medium confidence), uncertainties remain regarding the attribution of these 
changes to human activities. 

2. Recurring patterns of variability in large-scale atmospheric circulation (such as the North 
Atlantic Oscillation and Northern Annular Mode) and the atmosphere-ocean system 
(such as El Nino-Southern Oscillation) cause year-to-year variations in U.S. temperatures 
and precipitation ( high confidence). Changes in the occurrence of these patterns or their 
properties have contributed to recent U.S. temperature and precipitation trends ( medium 
confidence) although uncertainties remain about the size of the role of human influences 
in these changes. 

3. Increasing temperatures and atmospheric specific humidity are already having important 
influences on extremes {high confidence). It is still unclear, however, to what extent 
increasing temperatures and humidity have influenced and will influence persistent 
circulation patterns, which in turn influence these extremes. 


5.1. Introduction 

The causes of regional climate trends cannot be understood without considering the impact of 
changes in large-scale atmospheric circulation and an assessment of the role of internally 
generated climate variability. There are contributions to regional climate trends from changes in 
large-scale latitudinal circulation, which is generally organized into three cells in each 
hemisphere — Hadley Cell, Ferrell Cell and Polar Cell — and which determines the location of 
subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift 
poleward during warmer periods (Frierson et al. 2007; Sun et al. 2013; Vallis et al. 2015), which 
could result in poleward shifts in precipitation patterns affecting natural ecosystems, agriculture, 
and water resources (Seidel et al. 2008; Feng and Fu 2013). 

In addition, regional climate can be strongly affected by non-local response to recurring patterns 
(or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. 
These modes of variability represent preferred spatial patterns and their temporal variation and 
account for gross features in variance and for teleconnections. Modes of variability are often 
described as a product of a spatial climate pattern and an associated climate index time series that 
are identified based on statistical methods like Principle Component Analysis (PC analysis), 


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which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster 
analysis. 

[INSERT FIGURE 5.1 HERE: 

Figure 5.1: (Top) Plan and ( bottom ) cross section schematic view representation of the general 
circulation of the atmosphere. Three main circulations exist between the equator and poles due to 

solar heating and the earth’s rotation. 

Hadley cell (1) - Low-latitude air moves toward the equator. Due to solar heating, air near the 

equator rises vertically and moves poleward in the upper atmosphere. 

Ferrel cell (2) - A midlatitude mean atmospheric circulation cell. In this cell, the air flows 
poleward and eastward near the surface and equatorward and westward at higher levels. 

Polar cell (3) - Air rises, diverges, and travels toward the poles. Once over the poles, the air 
sinks, forming the polar highs. At the surface, air diverges outward from the polar highs. Surface 
winds in the polar cell are easterly (polar easterlies). 

A high pressure band is located at about 30° N/S latitude, leading to dry/hot weather due to 
descending air motion (subtropical dry zones are indicated in orange in the schematic views). 
Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the 
subtropical dry zones. A low pressure band is found at 50°-60° N/S, with rainy and stonny 
weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the 
atmosphere. (Figure source: adapted from NWS 2016)] 

On intra-seasonal to interannual time scales, the climate of the United States is strongly affected 
by modes of atmospheric circulation variability like the North Atlantic Oscillation 
(NAO)/Northem Annular Mode (NAM), North Pacific Oscillation (NPO), and Pacific North 
American Pattern (PNA). They are closely linked to other atmospheric circulation phenomena 
like blocking and quasi-stationary wave patterns and jet streams. On an interannual time scale, 
coupled atmosphere-ocean phenomena like El Nino-Southern Oscillation (ENSO) have a 
prominent effect. On longer time scales, U.S. climate anomalies are linked to slow variations of 
sea surface temperature related to the Pacific Decadal Oscillation (PDO) and the Atlantic 
Multidecadal Oscillation (AMO). 

In general, the influences of human activities on the climate system are now so widespread that 
the current and future behavior of these previous ‘natural’ climate features can no longer be 
assumed independent of those human influences. Climate response to external forcing appears to 
project strongly onto these existing recurring modes of variability, although the regional 
temperature and precipitation impacts of these modes can be modified due to a changed 
background climate. However, modes of internal variability of the climate system also contribute 
to observed decadal and multidecadal temperature and precipitation trends on local scales, 
masking possible systematic changes due to an anthropogenic influence. Recent studies point out 
though, that there are still large uncertainties in our understanding of the impact of human- 
induced climate change on atmospheric circulation, and the predictability of large-scale 
circulation changes might be limited (Shepherd 2014; Vallis et al. 2015). 


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5.2 Modes of Variability: Past and Projected Changes 

5.2.1 Width of the Tropics and Global Circulation 

Sea level pressure gives an indication of surface changes in atmospheric circulation. 
Contributions of greenhouse gas, ozone, and aerosol changes on the seasonal and geographical 
patterns of trends in global sea level pressure over 1951-2011 are detectable (Gillett et al. 2013). 
On regional scales and particularly at higher latitudes, internal variability has been found to play 
a large role in uncertainties of future sea level pressure projections (Deser et al. 2012). 

Evidence continues to mount for an expansion of the tropics over the past several decades, with a 
poleward expansion of the Hadley cell and an associated poleward shift of the subtropical dry 
zones in each hemisphere, although the rate of expansion is uncertain and depends on the metrics 
used (Bimer et al. 2014; Brdnnimann et al. 2015; Davis and Birner 2013; Feng and Fu 2013; 
Garfinkel et al. 2015; Karnauskas and Ummenhofer 2014; Fucas et al. 2014; Quan et al. 2014; 
Reichler 2016). While the roles of stratospheric ozone depletion in the Southern Hemisphere 
(Waugh et al. 2015) and anthropogenic aerosols in the Northern Hemisphere (Allen et al. 2012; 
Kovilakam and Mahajan 2015) have been implicated as contributors in the expansion, there is 
uncertainty in the relative contributions of natural and anthropogenic factors, and natural 
variability may be dominating (Adam et al. 2014; Allen et al. 2014; Garfinkel et al. 2015). 

Most of the previous work on tropical expansion to date has focused on zonally-averaged 
changes. There are only a few recent studies that diagnose regional characteristics of tropical 
expansion. The findings depend on analysis methods and datasets. For example, a northward 
expansion of the tropics in most regions of the Northern Hemisphere, including the Eastern 
Pacific with impact on drying in the American Southwest, is found based on diagnosing outgoing 
longwave radiation (Chen et al. 2014). However, other studies do not find a significant poleward 
expansion of the tropics over the Eastern Pacific and North America (Schwendike et al. 2015; 
Fucas and Nguyen 2015). Thus, the implications of the recent widening of the tropics for the 
climate of the United States and thus observed drying of the Southwest (Feng and Fu 2013; Prein 
et al. 2016) are not clear. 

Due to human-induced greenhouse gas increases, the Hadley cell is likely to widen in the future 
with an accompanying poleward shift in the subtropical dry zones and midlatitude jets (Collins et 
al. 2013; Barnes and Polvani 2013; Scheff and Frierson 2012a; Scheff and Frierson 2012b; Vallis 
et al. 2015; Feng and Fu 2013). Farge uncertainties remain in projected changes in non-zonal to 
regional circulation components and related changes in precipitation patterns (Simpson et al. 
2014; Barnes and Polvani 2013; Shepherd 2014; Simpson et al. 2016). Uncertainties in projected 
changes in midlatitude jets are also related to projected rate of arctic amplification and changes 
in the stratospheric polar vortex. Both factors could shift the mid-latitude jet equatorward 
especially in the North Atlantic region (Barnes and Polvani 2015; Scaife et al. 2012; Karpechko 
and Manzini 2012). 


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5.2.2 El Nino-Southern Oscillation 

El Nino-Southern Oscillation (ENSO) is a main source of climate variability, with a 2-7 year 
timescale, originating from coupled ocean-atmosphere interactions in the tropical Pacific and 
affecting weather patterns over many parts of the globe through atmospheric teleconnections. It 
strongly affects precipitation and temperature in the United States (Figure 5.2) (Halpert and 
Ropelewski 1992; Hoerling et al. 2001; Kiladis and Diaz 1989; Ropelewski and Halpert 1987). 

[INSERT FIGURE 5.2 HERE: 

Figure 5.2 : El Nino- and La Nina-related winter features over North America. Shown are typical 
January to March weather anomalies and atmospheric circulation during moderate to strong El 
Nino and La Nina conditions: (top) During El Nino, there is a tendency for a strong jet stream 
and stonn track across the southern part of the United States. The southern tier of Alaska and the 
U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier of U.S. states 
tends to be cooler and wetter than average. During La Nina, there is a tendency of a very wave- 
like jet stream flow over the United States and Canada, with colder and stormier than average 
conditions across the North, and warmer and less stormy conditions across the South. (Figure 
source: adapted from Lindsey 2016)] 

El Nino teleconnections are modulated by the location of maximum anomalous tropical Pacific 
sea surface temperatures (SST). Eastern Pacific (EP) El Nino events affect winter temperatures 
primarily over the Great Lakes, Northeast, and Southwest, while Central Pacific (CP) events 
influence temperatures primarily over the northwestern and southeastern United States (Yu et al. 

2012) . The CP El Nino also enhances the drying effect, but weakens the wetting effect, typically 
produced by traditional EP El Nino events on the United States winter precipitation (Yu and Zou 

2013) . It is not clear whether observed decadal-scale modulations of ENSO properties, including 
an increase in ENSO amplitude (Li et al. 2011) and an increase in frequency of CP El Nino 
events (Lee and McPhaden 2010; Yeh et al. 2009), are due to internal variability or 
anthropogenic forcing. There are at least a couple of reasons for this uncertainty. First, 
comprehensive observations that allow investigation of ENSO-related coupled atmosphere- 
ocean feedbacks go back only to the late 1970s (Christensen et al. 2013). Second, unforced 
global climate model simulations show that decadal to centennial modulations of ENSO can be 
generated without any change in external forcing (Capatondi et al. 2015). 

While there is high confidence that ENSO will remain a preferred mode of natural climate 
variability in the 21st century, only low confidence is indicated for specific projected changes in 
ENSO variability (Christensen et al. 2013). This low confidence is the result of the fact that 
models do not agree on the projected changes in El Nino intensity or on changes in the zonal 
gradient of tropical Pacific sea surface temperatures. Recent studies suggest a near doubling in 
frequency of occurrence of both extreme El Nino and La Nina events due to human-induced 
greenhouse gas increases for the 21st century relative to the 20th century, as determined by a 
subset of CMIP model simulations (Cai et al. 2014, 2015a,b). 


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There is robust evidence of an eastward shift of ENSO-induced teleconnection patterns due to 
greenhouse gas-induced climate change (Kug et al. 2010; Meehl and Teng 2007; Stevenson 
2012; Zhou et al. 2014). However, the impact of this shift on ENSO-induced climate anomalies 
in the United States is not well understood (Seager et al. 2012; Zhou et al. 2014). 

5.2.3 Extra-tropical Modes of Variability and Phenomena 

NORTH ATLANTIC OSCILLATION AND NORTHERN ANNULAR MODE 

The North Atlantic Oscillation (NAO), the leading recurring mode of variability in the extra- 
tropical North Atlantic region, describes an opposite variation in sea level pressure between the 
Atlantic subtropical high and the Iceland/ Arctic low. Variations in the NAO are accompanied by 
changes in the location and intensity of the Atlantic midlatitude storm track and blocking activity 
that affect climate over the North Atlantic and surrounding continents. A negative NAO phase is 
related to anomalously cold conditions and an enhanced number of cold outbreaks in the eastern 
United States, while a strong positive phase of the NAO tends to be associated with above- 
normal temperatures in this region (Hurrell and Deser 2009; Thompson and Wallace 2001). The 
positive phase of the NAO is associated with increased precipitation frequency and positive daily 
rainfall anomalies, including extreme daily precipitation anomalies in the northeastern United 
States (Durkee et al. 2008; Archambault et al. 2008). 

The Northern Annular Mode/ Arctic Oscillation (NAM/AO) is closely related to the NAO. It 
describes a pressure seesaw between mid and high latitudes on a hemispheric scale and thus 
includes a third anomaly center over the North Pacific Ocean (Thompson and Wallace 1998; 
Thompson and Wallace 2000). The time series of the NAO and NAM/AO are highly correlated, 
with persistent NAO and NAM/AO events being indistinguishable (Deser 2000; Feldstein and 
Franzke 2006). 

The wintertime NAO/NAM index exhibits pronounced variability on multidecadal time scales, 
with an increase from the 1960s to the 1990s, a shift to a more negative phase since the 1990s 
due to a series of winters like 2009-2010 and 2010-2011 (which had exceptionally low index 
values), and a return to more positive values after 2011 (Bindoff et al. 2013). Decadal scale 
temperature trends in the eastern United States, including occurrence of cold outbreaks during 
recent years, are linked to these changes in the NAO/NAM (Hurrell 1995; Cohen and Barlow 
2005; Overland and Wang 2015; Overland et al. 2015). 

The CMIP5 models on average simulate a progressive shift of the NAO/NAM towards its 
positive phase due to human-induced climate change (Gillett and Fyfe 2013). However, the 
spread between model simulations is larger than the projected multimodel increase, and shifts 
between preferred periods of positive and negative NAO phase will continue to occur similar to 
those observed in the past (Deser et al. 2012; Christensen et al. 2013). 


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The NAO’s influence on the ocean occurs through changes in heat content, gyre circulations, 
mixed layer depth, salinity, high-latitude deep water formation, and sea ice cover (Hurrell and 
Deser 2009). Climate model simulations show that multidecadal variation in the NAO induce 
multidecadal variations in the Atlantic meridional overturning circulation and poleward ocean 
heat transport in the Atlantic that is extending to the Arctic. It has been suggested that these 
variations have contributed to the observed rapid loss of Arctic sea ice and Northern Hemisphere 
warming, especially in the late 1990s and early 2000s, and thus enhanced the long-tenn trends in 
Arctic sea ice loss and hemispheric warming that are mainly caused by anthropogenic forcing 
(Delworth et al. 2016). 

NORTH PACIFIC OSCILLATION/WEST PACIFIC OSCILLATION 

The North Pacific Oscillation (NPO) is the leading mode of variability in the extratropical North 
Pacific region and is characterized by a north-south seesaw in sea level pressure. NPO effects on 
U.S. hydroclimate and marginal ice zone extent in the Arctic seas have been reported (Linkin 
and Nigam 2008). However, 21st century climate projections suggest no major changes in the 
NPO (Furtado et al. 2011). 

PACIFIC/NORTH AMERICAN PATTERN 

The Pacific/North American (PNA) pattern is the leading recurring mode of internal atmospheric 
variability over the North Pacific and the North American continent, especially during the cold 
season. It describes a quadripole pattern of mid-tropospheric height anomalies, with anomalies of 
similar sign located over the subtropical northeastern Pacific and northwestern North America 
and of the opposite sign centered over the Gulf of Alaska and the southeastern United States. The 
PNA pattern is associated with strong fluctuations in the strength and location of the East Asian 
jet stream. The positive phase of the PNA pattern is associated with above-average temperatures 
over the western and northwestern United States, and below-average temperatures across the 
south-central and southeastern United States, including enhanced occurrence of extreme cold 
temperatures (Leathers et al. 1991; Loikith and Broccoli 2012; Ning and Bradley 2016). 
Significant negative correlation between the PNA and winter precipitation over the Ohio River 
Valley has been documented (Leathers et al. 1991; Coleman and Rogers 2003; Ning and Bradley 
2016). 

The PNA is related to ENSO events (Nigam 2003) and also serves as a bridge linking ENSO and 
NAO variability (Li and Lau 2012). A single model sensitivity study suggests that the PNA 
mode of atmospheric internal variability remains largely unchanged in pattern in a wanner 
climate (Zhou et al. 2014). 

BLOCKING AND QUASI-STATIONARY WAVES 

Anomalous atmospheric flow patterns in the extratropics that remain in place for an extended 
period of time (for example, blocking and quasi-stationary Rossby waves) — and thus affect a 


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1 region with similar weather conditions like rain or clear sky for several days to weeks — can lead 

2 to flooding, drought, heat waves, and cold waves (Grotjahn et al. 2016; Whan et al. 2016; 

3 Petoukhov et al. 2013). Specifically, blocking describes large-scale, persistent high pressure 

4 systems that interrupt the typical westerly flow, while planetary waves (Rossby waves) describe 

5 large meandering of the atmospheric jet stream. 

6 A persistent pattern of high pressure in the circulation off the west coast of the United States has 

7 been associated with the California drought (Ch.8; Swain et al. 2014; Seager et al. 2015). 

8 Blocking in the Alaskan region, which is enhanced during La Nina winters (Figure 5.2) 

9 (Renwick and Wallace 1996), is associated with higher temperatures in western Alaska but a 

10 shift to lower mean and extreme surface temperatures from the Yukon southward to the southern 

1 1 Plains (Carrera et al. 2004). The anomalously cold winters of 2009-2010 and 2010-2011 in the 

12 United States are linked to the blocked (or negative) phase of the NAO (Guirguis et al. 2011). 

13 Stationary Rossby wave patterns may have contributed to the North American temperature 

14 extremes during summers like 2011 (Wang et al. 2014). It has been suggested that arctic 

15 amplification has already led to weakened westerly winds and hence more slowly moving and 

16 amplified wave patterns and enhanced occurrence of blocking (Francis and Vavrus 2012; Francis 

17 et al. 2016; Ch. 11: Arctic Changes). 

18 While a study based on a homogenized/extended Greenland Blocking Index (GBI) identified a 

19 significant increase in the frequency of blocking events over Greenland since 1981 in all seasons 

20 as well as in the annual mean (Hanna et al. 2016), a series of other studies did not find a 

21 significant increase in the frequency of blocking, based on various blocking metrics in specific 

22 regions and seasons and on various reanalysis products (Barnes 2013; Barnes et al. 2014). 

23 Various metrics have been applied to diagnose recent changes in the amplitude of midlatitude 

24 planetary waves (Francis and Vavrus 2012; Screen and Simmonds 2013) that differ in their 

25 conclusions. While a metric based on the maximum latitude of selected 500 mb geopotential 

26 height (Z500) isopleths exhibits a statistical significant increase (Francis and Vavrus 2012), a 

27 metric based on midlatitude meridional wave amplitude at 500 mb does not show significant 

28 changes (Screen and Simmonds 2013). 

29 A decrease of blocking frequency with climate change is found in CMIP3, CMIP5, and higher- 

30 resolution models (Christensen et al. 2013; Hoskins and Woollings 2015, Kennedy et al. 2016). 

3 1 However, CMIP5 models still underestimate observed blocking activity in the North Atlantic 

32 sector while they tend to overestimate activity in the North Pacific, although with a large 

33 intermodel spread (Christensen et al. 2013). Climate models robustly project a change in 

34 Northern Hemisphere winter quasi-stationary wave fields that are linked to a wetting of the 

35 North American West Coast (Brandefelt and Komich 2008; Haarsma and Selten 2012; Simpson 

36 et al. 2014), due to a strengthening of the zonal mean westerlies in the subtropical upper 

37 troposphere. However, most of the climate models are found to overestimate the climate change 

38 related response because of biases in the representation of relevant waves (Simpson et al. 2016). 


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Therefore, there is low confidence in projected changes in atmospheric blocking and wintertime 
quasi- stationary waves. 

5.2.4 Modes of Variability on Decadal to Multidecadal Time Scales 

PACIFC DECADAL OSCILLATION (PDO)/INTERDECADAL PACIFIC 
OSCILLATION (IPO) 

The Pacific Decadal Oscillation (PDO) is the leading year-round pattern of monthly North 
Pacific sea surface temperature variability, with a characteristic time scale of 40 to 60 years. 
Interdecadal Pacific Oscillation (IPO) refers to the same phenomenon based on Pacific-wide sea 
surface temperatures. PDO/IPO represents not a single phenomenon but rather a combination of 
processes that span the tropics and extratropics, including both remote tropical forcing and local 
North Pacific atmosphere-ocean interactions (Newman et al. 2016). Consequently, PDO-related 
impacts on temperature and precipitation of the United States are very similar to phenomena on 
interannual time scales like ENSO and variations in the strength of the Aleutian low (North 
Pacific Index, NPI), as shown in Figure 5.3. A PDO-related impact on Alaska temperatures is 
also apparent (Hartmann and Wendler 2005; McAfee 2014). 

[INSERT FIGURE 5.3 HERE: 

Cold season relationship between climate indices and U.S. precipitation and temperature 
anomalies determined from U.S. climate division data (Vose et al. 2014), for the years 1 901— 
2014. November-March mean U.S. precipitation anomalies correlated with (a) the PDO index, 
(b) the ENSO index, and (c) the North Pacific Index. November-March U.S. temperature 
anomalies correlated with (d) the PDO index, (e) the ENSO index, and (f) the NPI. Decadal 
impacts related to the Pacific Decadal Oscillation (PDO) on temperature and precipitation of the 
United States are very similar to the impact of phenomena on interannual time scales like ENSO 
and variations in the strength of the Aleutian low characterized by the North Pacific Index (NPI). 
(Figure source: Newman et al. 2016; © American Meteorological Society, used with 
permission)] 

Studies on future changes in the PDO/IPO are available based on CMIP3 models. It is found that 
most of these models do not exhibit significant changes in spatial and temporal characteristics in 
the PDO/IPO (Furtado et al. 2011), while some models suggest that the PDO/IPO becomes 
weaker and more frequent by the end of the 21st century (Lapp et al. 2012). Future emission 
changes have been suggested to also impact the PDO/IPO (Allen and Ajoku 2016). Therefore, 
there is only low confidence in projected future changes in the PDO/IPO. 

ATLANTIC MULTI-DECADAL OSCILLATION (AMO) 

The Atlantic Multi-Decadal Oscillation (AMO) is one of the principal features of multidecadal 
variability in the instrumental climate record, with a coherent pattern of 50- to 70-year variability 
in surface temperature centered on the North Atlantic Ocean. Cool AMO phases occurred in the 


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1 1900s-1920s and 1960s-1980s, while a warm phase occurred in the 1930s-1950s and has been 

2 observed since the mid-1990s. During AMO wann periods, less than normal precipitation is 

3 found in most of the United States, including the most severe 20th century droughts in the 1930s 

4 and 1950s (Enfield et al. 2001; Seager et al. 2008; Feng et al. 2011). It is suggested that the 

5 wann phase of the AMO strengthens the North Atlantic tropical cyclone activity (Goldenberg et 

6 al. 2001; Chylek and Lesins 2008; Zhang and Delworth 2009). 

7 Long-lived Atlantic multidecadal variability is found in long control simulations earned out with 

8 climate models (Menary et al. 2012), and CMIP3 models do not show any fundamental change 

9 in the characteristics of the AMO in the 2 1 st century as compared to the 20th century or 

10 preindustrial climate (Ting et al. 2011). 

1 1 INTERNALLY-GENERATED VERSUS EXTERNALLY-FORCED DECADAL 

12 CLIMATE VARIABILITY 

13 Several studies suggest that climate patterns in response to natural forcings (such as volcanic 

14 aerosols) and anthropogenic forcings (such as aerosols and greenhouse gases) project onto 

15 AMO- and PDO/IPO-related climate variability patterns (Boo et al. 2015; Booth et al. 2012; 

16 Evan et al. 2009; Mann and Emanuel 2006; Meehl et al. 2013). For example, historical aerosol 

17 cooling combined with global ocean wanning due to increasing greenhouse gases could explain 

18 a large fraction of Atlantic multidecadal variability (Booth et al. 2012). Changes in aerosols are 

19 also found to coincide with PDO-like variability in North Pacific sea surface temperatures (Boo 

20 et al. 2015). Furthermore, it has been determined that periods with near zero warming trends of 

2 1 global mean temperature and periods of accelerated temperatures result from the interplay 

22 between internally generated PDO/IPO-like cooling and wanning in the tropical Pacific Ocean 

23 and greenhouse gas-induced ocean warming (Meehl et al. 2013). These findings have 

24 implications for the attribution of causes of trends in global and regional mean temperatures, 

25 width of the tropics, droughts, and tropical cyclones (Ch. 1, 3, 8, 9 and Section 5.2. 1). For 

26 example, studies that assign an entirely natural forcing component to regional patterns that 

27 resemble PDO/IPO may underestimate the role of human forcing, while studies that did not 

28 account for the impact of the PDO/IPO may overestimate the role of human-induced forcing 

29 (Abatzoglou et al. 2014a; Abatzoglou et al. 2014b; Johnstone and Mantua 2014a; Johnstone and 

30 Mantua 2014b). Furthermore, it is likely that PDO/IPO and AMO-like variability will continue 

31 to occur in the future, modulating anthropogenic forcing and its climate impacts on the United 

32 States and globally. 

33 5.3. Quantifying the Role of Internal Variability on Past and Future U.S. 

34 Climate Trends 

35 The role of internal variability in masking trends is substantially increased on regional and local 

36 scales relative to the global scale, and in the extratropics relative to the tropics (Ch. 4: 

37 Projections). Approaches are developed to better quantify the externally forced and internally 


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26 

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driven contributions to observed and future climate trends and variability and further separate 
these contributions into thermodynamically and dynamically driven factors (Wallace et al. 2015). 
Specifically, large “initial condition” climate model ensembles with 30 ensemble members and 
more (Deser et al. 2012; Deser et al. 2014; Wettstein and Deser 2014) and long control runs 
(Thompson et al. 2015) have been shown to be useful tools to characterize uncertainties in 
climate change projections at local/regional scales. 

North American temperature and precipitation trends on timescales of up to a few decades are 
strongly affected by intrinsic atmospheric circulation variability (Deser et al. 2014; Wallace et al. 
2015; Deser et al. 2016). For example, it is estimated that internal circulation trends account for 
approximately one-third of the observed wintertime wanning over North America during the past 
50 years. In a few areas, such as the central Rocky Mountains and far western Alaska, internal 
dynamics have offset the warming trend by 1 0%— 30% (Deser et al. 2016). Natural climate 
variability superimposed upon forced climate change will result in a large range of possible 
trends for surface air temperature and precipitation in the United States over the next 50 years 
(Figure 5.4) (Deser et al. 2014). 

[INSERT FIGURE 5.4 HERE: 

Figure 5.4: (left) Total 2010-60 winter trends decomposed into (center) internal and (right) 
forced components for two contrasting CCSM3 ensemble members (runs 29 and 6) for (a) SAT 
[color shading; °F (51 years) -1 ] and SLP (contours) and (b) precipitation [color shading; inches 
per day (51 years) -1 ] and SLP (contours). SLP contour interval is 1 hPa (51 years) -1 , with solid 
(dashed) contours for positive (negative) values; the zero contour is thickened. The same climate 
model (CCSM3) simulates a large range of possible trends in North American climate over the 
2010 -2060 period because of the influence of internal climate variability superposed upon forced 
climate trends. (Figure source: adapted from Deser et al. 2014; © American Meteorological 
Society, used with permission)] 

Climate models are evaluated with respect to their proper simulation of internal decadal 
variability. Comparing observed and simulated variability estimates at time scales longer than 10 
years suggest that models tend to overestimate the internal variability in the northern 
extratropics, including over the continental United States, but underestimate it over much of the 
tropics and subtropical ocean regions (Deser et al. 2012; Knutson et al. 2013). Such biases affect 
signal-to-noise estimates of regional scale climate change response and thus assessment of 
internally driven contributions to regional/local trends. 


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1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 Under increased greenhouse gas concentrations, the tropics are likely to expand with an 

4 accompanying poleward shift of the subtropical dry zones and midlatitude jets in each 

5 hemisphere ( medium to high confidence). While it is likely that tropics have expanded since 1979 

6 ( medium confidence), uncertainties remain regarding the attribution of these changes to human 

7 activities. 

8 Description of evidence base 

9 The Key Finding is supported by statements of the previous international IPCC AR5 assessment 

10 (IPCC 2013). Further evidence of an impact of greenhouse gas increases on the widening of the 

1 1 tropical belt and poleward shifts of the mid-latitude jets is provided by the diagnosis of CMIP5 

12 simulations (Vallis et al. 2015, Barnes and Polvani 2013). Recent studies on estimates of changes 

13 in the width of the tropics provide additional evidence that the tropics has widened since 1979 

14 (Birner et al. 2014; Davis and Birner 2013; Feng and Fu 2013; Garfinkel et al. 2015; Karnauskas 

15 and I Jmmenhofer 2014; Lucas et al. 2014; Quan et al. 2014; Reichler 2016). Recent studies 

16 provide new evidence on the significance of internal variability on recent changes in the tropical 

17 width (Adam et al. 2014; Allen et al. 2014; Garfinkel et al. 2015). These studies are discussed in 

1 8 the text. 

19 Major uncertainties 

20 The rate of observed expansion of tropics is uncertain and depends on the metrics used. 

2 1 Uncertainties also result from the utilizing of reanalysis to detennine trends and limited 

22 observational records. There are major uncertainty in the estimates of the relative contribution of 

23 anthropogenic factors and internal variability to recent trends. Uncertainties in modeling future 

24 changes in global circulation arises from the presentation of stratosphere as well as simulated 

25 Arctic amplification. 

26 Assessment of confidence based on evidence and agreement, including short description of 

27 nature of evidence and level of agreement 

28 ElVery High 

29 x High 

30 x Medium 

3 1 x Low 

32 There is high confidence that increased greenhouse gases cause a poleward expansion of the 

33 Hadley circulation. This is based on the agreement of a large number of studies utilizing 

34 modeling of different complexity and theoretical considerations. There is only medium 

35 confidence in future changes of mid-latitude jets specifically in the Northern Hemisphere due to 

36 the potential impact of other factors (Arctic amplification, stratospheric circulation change) that 

37 can push the mid-latitude jets equatorward. Uncertainties in the causes of recent trends result 


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Chapter 5 


1 from uncertainties in the magnitude of observed widening and a possibly large contribution of 

2 internal variability. 

3 If appropriate, estimate likelihood of impact or consequence, including short description of 

4 basis of estimate 

5 □ Greater than 9 in 10 / Very Likely 

6 x Greater than 2 in 3 / Likely 

7 □ About 1 in 2 / As Likely as Not 

8 □ Less than 1 in 3 / Unlikely 

9 □ Less than 1 in 10 / Very Unlikely 

10 Estimate is based on the assessment of a large number of studies that diagnose past changes in 

1 1 global circulation and the impact of increased greenhouse gas concentration on tropical width 

12 and mid-latitude jets. A poleward shift of global circulation results in poleward expansion of 

13 tropical dry zones and mid-latitude circulation patterns that affect natural ecosystems, 

14 agriculture, and water resources. 

15 Summary sentence or paragraph that integrates the above information 

16 This Key Finding is supported by a large amount of observational and modeling evidence 

17 documented in the climate science peer-reviewed literature. Compared to the previous 

18 international assessment (IPCC AR5) the confidence is increased for an observed poleward shift 

19 of circulation features since 1979 due to additional observational studies. Uncertainties regarding 

20 the attribution of the observed tropical widening results from both uncertainties in the magnitude 

21 of observed trends and the contribution of internal variability. 

22 

23 Key Finding 2 

24 Recurring patterns of variability in large-scale atmospheric circulation (such as the North 

25 Atlantic Oscillation and Northern Annular Mode) and the atmosphere-ocean system (such as El 

26 Nino-Southern Oscillation) cause year-to-year variations in U.S. temperatures and precipitation 

27 (high confidence). Changes in the occurrence of these patterns or their properties have 

28 contributed to recent U.S. temperature and precipitation trends (medium confidence) although 

29 uncertainties remain about the size of the role of human influences in these changes. 

30 Description of evidence base 

3 1 The Key Finding is supported by multiple studies as described in the text that diagnose recurring 

32 patterns of variability and their changes, as well as their impact on temperature and precipitation 

33 of the United States. These included studies on changes in the Northern Atlantic 

34 Oscillation/Northern Annular Mode (Hurrell 1995; Cohen and Barlow 2005, Overland and Wang 

35 2015; Overland et al. 2015) as well as studies on the observed decadal modification of ENSO 

36 (Yu et al. 2012; Yu and Zou 2013; Li et al. 2011; Lee and McPhaden 2010; Yeh et al. 2009; 

37 Capantoni et al. 2013). The uncertainties in the attribution of changes in these preferred patterns 


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1 of variability results from limited observational records and the findings from long climate 

2 simulations showing that decadal to multi-decadal variations of El Nino-Southem Oscillation 

3 and Northern Annular Mode can be generated without any change in external forcing (Capantoni 

4 et al. 2013; Deser et al. 2012; IPCC 2013). These studies are discussed in the text. 

5 Major uncertainties 

6 A key uncertainty is related to limited observational records and our capability to proper simulate 

7 climate variability on decadal to multidecadal time scale, as well as properly simulate modes of 

8 climate variability including El Nino-Southern Oscillation and Northern Hemisphere Annular 

9 Mode-North Atlantic Oscillation. 

10 Assessment of confidence based on evidence and agreement, including short description of 

1 1 nature of evidence and level of agreement 

12 □ Very High 

13 x High 

14 x Medium 

15 □ Low 

16 There is high confidence that preferred modes of variability affect U.S. temperature on year-to- 

17 year time scale and medium confidence on their impact on decadal time scales based on a large 

18 number of studies that diagnose observational data records and long simulations with climate 

19 models for a various of preferred modes of variability. 

20 If appropriate, estimate likelihood of impact or consequence, including short description of 

2 1 basis of estimate 

22 □ Greater than 9 in 10 / Very Likely 

23 □ Greater than 2 in 3 / Likely 

24 □ About 1 in 2 / As Likely as Not 

25 □ Less than 1 in 3 / Unlikely 

26 □ Less than 1 in 10 / Very Unlikely 

27 Summary sentence or paragraph that integrates the above information 

28 The Key binding is supported by multiple studies that diagnose recurring patterns of variability 

29 and their changes, as well as their impact on temperature and precipitation of the United States. 

30 The causes of these changes are uncertain due to the limited observational record. 

31 

32 Key Finding 3 

33 Increasing temperatures and atmospheric specific humidity are already having important 

34 influences on extremes (high confidence). It is still unclear, however, to what extent increasing 

35 temperatures and humidity have influenced and will influence persistent circulation patterns, 

36 which in turn influence these extremes. 


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Chapter 5 


1 Description of evidence base 

2 The Key Finding integrates assessment from Chapters 5 and 6, regarding the impact of 

3 increasing temperatures and atmospheric specific humidity on extremes with the assessment of 

4 this chapter on circulation changes. The key finding on the low confidence the impact of 

5 increasing temperatures and humidity on quasi-persistent circulation patterns supported by 

6 statements of the previous international assessment (IPCC 2013) and recent studies on changes 

7 in atmospheric blocking and stationary waves in observation and climate models (Barnes 2013; 

8 Barnes et al. 2014, Francis and Vavrus 2012; Screen and Simmonds 2013; Simpson et al. 2016; 

9 Kennedy et al. 2016), and theoretical considerations (Hoskins and Woollings 2015). These 

10 studies are discussed in the text. 

1 1 Major uncertainties 

12 Key uncertainties result from the lack of climate models to properly simulate quasi-stationary 

13 circulation patterns like atmospheric blocking and quasi-stationary waves and limited records of 

14 observations. 

15 Assessment of confidence based on evidence and agreement, including short description of 

16 nature of evidence and level of agreement 

17 □ Very High 

1 8 x High 

19 □ Medium 

20 x Low 

21 Low confidence in the impact of changes of temperature and specific humidity on circulation 

22 patterns results from the lack of detectability of robust trends of changes in persistent circulation 

23 patterns in observational records as well as model biases that limit the confidence in projected 

24 trends. 

25 If appropriate, estimate likelihood of impact or consequence, including short description of 

26 basis of estimate 

27 □ Greater than 9 in 10 / Very Likely 

28 □ Greater than 2 in 3 / Likely 

29 □ About 1 in 2 / As Likely as Not 

30 □ Less than 1 in 3 / Unlikely 

31 □ Less than 1 in 10 / Very Unlikely 

32 Summary sentence or paragraph that integrates the above information 

33 Our confidence is low on the impact of changes in observed and future changes in temperature 

34 and specific humidity on persistent circulation patterns that could affect extremes. Uncertainty is 

35 large because of lack of robust detectability of past trends in these persistent circulation patterns 

36 and model biases in simulating these patterns that limit our confidence in simulated changes. 


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Chapter 5 


1 FIGURES 


2 

3 

4 

5 

6 

7 

8 
9 


Atmospheric Circulation 



90°N 

H 

Dry and stable 


60°N 

L 


30°N 
Expanding || 

Dry 

and stable 


I Tropics 


0 ° 

L 


Figure 5.1:(Top) Plan and ( bottom ) cross section schematic view representation of the general 
circulation of the atmosphere. Three main circulations exist between the equator and poles due to 
solar heating and the earth’s rotation. 

Hadley cell (1) - Low-latitude air moves toward the equator. Due to solar heating, air near the 
equator rises vertically and moves poleward in the upper atmosphere. 

Ferrel cell (2) - A midlatitude mean atmospheric circulation cell. In this cell, the air flows 
poleward and eastward near the surface and equatorward and westward at higher levels. 


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1 Polar cell (3) - Air rises, diverges, and travels toward the poles. Once over the poles, the air 

2 sinks, forming the polar highs. At the surface, air diverges outward from the polar highs. Surface 

3 winds in the polar cell are easterly (polar easterlies). 

4 A high pressure band is located at about 30° N/S latitude, leading to dry/hot weather due to 

5 descending air motion (subtropical dry zones are indicated in orange in the schematic views). 

6 Expanding tropics (indicted by orange arrows) are associated with a poleward shift of the 

7 subtropical dry zones. A low pressure band is found at 50°-60° N/S, with rainy and stonny 

8 weather in relation to the polar jet stream bands of strong westerly wind in the upper levels of the 

9 atmosphere. (Figure source: adapted from NWS 2016) 

10 


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3 

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5 

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7 

8 

9 

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Chapter 5 



Figure 5.2: El Nino- and La Nina-related winter features over North America. Shown are typical 
January to March weather anomalies and atmospheric circulation during moderate to strong El 
Nino and La Nina conditions: (top) During El Nino, there is a tendency for a strong jet stream 
and stonn track across the southern part of the United States. The southern tier of Alaska and the 
U.S. Pacific Northwest tend to be warmer than average, whereas the southern tier of U.S. states 
tends to be cooler and wetter than average. During La Nina, there is a tendency of a very wave- 
like jet stream flow over the United States and Canada, with colder and stormier than average 
conditions across the North, and warmer and less stormy conditions across the South. (Figure 
source: adapted from Lindsey 2016) 


TYPICAL EL NINO WINTERS 


low pressure 


TYPICAL LA NINA WINTERS 


extended 
Pacific Jet Stream, 
amplified storm 
track 


Polar Jet Stream 


blocking 
high pressure 


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2 

3 

4 

5 

6 

7 

8 

9 

10 

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Chapter 5 




Cold Season Relationship 

between Climate Indices and Precipitation/Temperature Anomalies 

Nov-Mar Precipitation Nov-Mar Temperature 

d) 


Correlation Coefficient 


Figure 5.3: Cold season relationship between climate indices and U.S. precipitation and 
temperature anomalies determined from U.S. climate division data (Vose et al. 2014), for the 
years 1901-2014. November-March mean U.S. precipitation anomalies correlated with (a) the 
PDO index, (b) the ENSO index, and (c) the North Pacific Index. November-March U.S. 
temperature anomalies correlated with (d) the PDO index, (e) the ENSO index, and (f) the NPI 
Decadal impacts related to the Pacific Decadal Oscillation (PDO) on temperature and 
precipitation of the United States are very similar to the impact of phenomena on interannual 
time scales like ENSO and variations in the strength of the Aleutian low characterized by the 
North Pacific Index (NPI). (Figure source: Newman et al. 2016; © American Meteorological 
Society, used with permission) 


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a) Winter surface air temperature and sea level pressure 




Temperature Change (°F) 



-12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 


b) Winter precipitation and sea level pressure 



Precipitation Change (in) 


1 -0.05 -0.03 -0.02 -0.01 0.00 0.01 0.02 0.03 0.05 

2 Figure 5.4: (left) Total 2010-2060 winter trends decomposed into (center) internal and (right) 

3 forced components for two contrasting CCSM3 ensemble members (runs 29 and 6) for (a) SAT 

4 [color shading; °F (51 years)" 1 ] and SLP (contours) and (b) precipitation [color shading; inches 

5 per day (5 1 years) "'] and SLP (contours). SLP contour interval is 1 hPa (5 1 years) with solid 

6 (dashed) contours for positive (negative) values; the zero contour is thickened. The same climate 

7 model (CCSM3) simulates a large range of possible trends in North American climate over the 

8 2010-2060 period because of the influence of internal climate variability superposed upon forced 

9 climate trends. (Figure source: adapted from Deser et al. 2014; © American Meteorological 

10 Society, used with permission.) 

11 




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1 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53, 

2 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248T 

3 Wallace, J.M., C. Deser, B .V. Smoliak, and A.S . Phillips, 2015: Attribution of Climate Change 

4 in the Presence of Internal Variability. Climate Change: Multidecadal and Beyond. WORLD 

5 SCIENTIFIC, 1-29. http://dx.doi.org/10.1142/9789814579933_0001 

6 Wang, H., S. Schubert, R. Koster, Y.-G. Ham, and M. Suarez, 2014: On the Role of SST Forcing 

7 in the 2011 and 2012 Extreme U.S. Heat and Drought: A Study in Contrasts. Journal of 

8 Hydrometeorology , 15, 1255-1273. http://dx.doi.org/10.1175/JHM-D-13-069T 

9 Waugh, D.W., C.I. Garfinkel, and F.M. Polvani, 2015: Drivers of the Recent Tropical Expansion 

10 in the Southern Hemisphere: Changing SSTs or Ozone Depletion? Journal of Climate, 28, 

11 6581-6586. http://dx.doi.org/10.1175/JCLI-D-15-0138T 

12 Wettstein, J.J. and C. Deser, 2014: Internal Variability in Projections of Twenty-First-Century 

13 Arctic Sea Ice Loss: Role of the Large-Scale Atmospheric Circulation. Journal of Climate, 

14 27, 527-550. http://dx.doi.org/10.1175/JCLI-D-12-00839T 

15 Whan, K., F. Zwiers, and J. Sillmann, 2016: The Influence of Atmospheric Blocking on Extreme 

1 6 Winter Minimum Temperatures in North America. Journal of Climate, 29, 436 1 -4381 . 

17 http://dx.doi.org/10.1175/JCLI-D-15-0493T 

18 Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B.P. Kirtman, and F.-F. Jin, 2009: El Nino in a 

19 changing climate. Nature, 461, 511-514. http://dx.doi.org/10.1038/nature08316 

20 Yu, J.-Y. and Y. Zou, 2013: The enhanced drying effect of Central-Pacific El Nino on US 

21 winter. Environmental Research Letters, 8, 014019. http://dx.doi.org/10.1088/1748- 

22 9326/8/1/014019 

23 Yu, J.-Y., Y. Zou, S.T. Kim, and T. Lee, 2012: The changing impact of El Nino on US winter 

24 temperatures. Geophysical Research Letters, 39, LI 5702. 

25 http://dx.doi.org/10.1029/2012GL052483 

26 Zhang, R. and T.L. Delworth, 2009: A new method for attributing climate variations over the 

27 Atlantic Hurricane Basin's main development region. Geophysical Research Letters, 36, 

28 L06701. http://dx.doi.org/10.1029/2009GL037260 

29 Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global Warming-Induced 

30 Changes in El Nino Teleconnections over the North Pacific and North America. Journal of 

3 1 Climate, 27, 9050-9064. http://dx.doi.org/10T 175/JCLI-D- 14-00254.1 


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l 6. Temperature Changes in the United States 


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KEY FINDINGS 

1 . The annual-average, near-surface air temperature over the contiguous United States has 
increased by about 1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both 
show rapid warming since the late 1970s, while paleo-temperature evidence shows that 
recent decades have been the warmest in at least the past 1 ,500 years. ( Extremely likely, 
High confidence) 

2. Accompanying the rise is average temperatures, there have been - as is to be expected - 
increases in extreme temperature events in most parts of the United States. Since the 
early 1900s, the temperature of extremely cold days has increased throughout the 
contiguous United States, and the temperature of extremely wann days has increased 
across much of the West. In recent decades, intense cold waves have become less 
common while intense heat waves have become more common. (. Extremely likely, Very 
high confidence) 

3. The average annual temperature of the contiguous United States is projected to rise 
throughout the century. Increases of at least 2.5°F (1.4°C) are projected over the next few 
decades, meaning that recent record-setting years will be relatively “common” in the near 
future. Increases of 5.0°-7.5°F (2.8°-4.8°C) are projected by late century depending upon 
the level of future emissions. (. Extremely likely, Very high confidence) 

4. Extreme temperatures are projected to increase even more than average temperatures. 

The temperatures of extremely cold days and extremely warm days are both projected to 
increase. Cold waves are projected to become less intense while heat waves will become 
more intense. (. Extremely likely, Very high confidence ) 

Introduction 

Temperature is among the most important climatic elements used in decision-making. For 
example, builders and insurers use temperature data for planning and risk management. Energy 
companies and regulators use temperature data to predict demand and set utility rates. Fanners 
use temperature data to select crop types and detennine planting times. 

Temperature is also a key indicator of climate change: recent increases are apparent over the 
land, ocean, and troposphere, and substantial changes are expected for this century. This chapter 
summarizes the major observed and projected changes in near-surface air temperature over the 
United States, emphasizing new data sets and model projections since NCA3. Changes are 
depicted using a spectrum of observations, including surface weather stations, moored ocean 
buoys, polar-orbiting satellites, and temperature-sensitive proxies. Projections are based on 
global models and downscaled products from CMIP5 (Coupled Model Intercomparison Project 


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Phase 5) using a suite of Representative Concentration Pathways (RCPs; see Chapter 4 for more 
on RCPs and future scenarios). 

6.1 Historical Changes 

6.1.1. Average Temperatures 

Changes in temperature are described using a suite of observational datasets. As in the Third 
National Climate Assessment (NCA3), the primary dataset for the contiguous United States is 
nClimGrid (Vose et al. 2014), but new datasets are now available to address changes in Alaska, 
Hawai‘i, and the Caribbean (Vose et al. submitted). Along U.S. coastlines, changes in sea surface 
temperatures are quantified using a new reconstruction (Huang et al. 2015), which now forms the 
ocean component of the NOAA Global Temperature dataset (Vose et al. 2012). Changes in 
middle tropospheric temperature are assessed using several recently improved satellite datasets. 

Average annual temperature across the United States increased by about 0.7°C (1.2°F) between 
1901 and 2015, very slightly less than reported inNCA3 (Table 6.1). This difference stems from 
the use of different time periods to represent present-day climate in each report. In particular, 
NCA3 defined present-day as the average of 1991-2012, which was slightly warmer than the 
1986-2015 period used here. (The reference period in both assessments is 1901-1960.) 

[INSERT TABLE 6.1 HERE: 

Table 6.1. Observed changes in average annual temperature (°F) for each NCA region. Changes 
are the difference between the average for present-day (1986-2015) and the average for the first 
half of the last century (1901-1960)]. 

Each NCA region experienced a net warming through 2015 (Table 6. 1). The largest changes 
were in the western United States, where average temperature increased by more than 0.8°C 
(1.50°F) in Alaska, the Northwest, the Southwest, and also in the Northern Great Plains. As 
noted in NCA3, the Southeast had the least warming, driven by a combination of natural 
variations and human influences. Across all regions, average minimum temperature increased at 
a slightly higher rate than average maximum temperature, with the Midwest having the largest 
discrepancy. This differential rate of warming resulted in a continuing decrease in the diurnal 
temperature range that is consistent with other parts of the globe (Thorne et al. 2016). Average 
sea surface temperature also increased along all regional coastlines (see Figure 1.3), though 
changes were generally smaller than over land owing to the higher heat capacity of water. 
Increases were largest in Alaska (greater than 0.6°C [1 .0°F]) while increases were smallest (less 
than 0.3°C [0.5°F]) in coastal areas of the Southeast. 

More than 97% of the land surface of the United States had an increase in average temperature 
from 1900 to 2015 (Figure 6.1). In contrast, only small (and somewhat dispersed) parts of the 
Southeast and Southern Great Plains experienced cooling. From a seasonal perspective, warming 
was greatest and most widespread in winter, with increases of over 0.8°C (1.5°F) in most areas. 


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In summer, warming was less extensive (mainly along the East Coast and in the western third of 
the Nation), while cooling was evident in parts of the Southeast, Midwest, and Great Plains. 

[INSERT FIGURE 6.1 HERE: 

Figure 6.1. Observed changes in annual, winter, and summer temperature (°F). Observed 

changes in annual, winter, and summer temperature (°F). Changes are the difference between the 

average for present-day (1986-2015) and the average for the first half of the last century (1901- 
1960 for the contiguous United States, 1925-1960 for Alaska and Hawai‘i). (Figure source: 
NOAA/NCEI)]. 

There has been a rapid increase in the average temperature of the contiguous United States over 
the past several decades. There is general consistency on this point between the surface 
thennometer record from NOAA (Vose et al. 2014) and the middle tropospheric satellite records 
from Remote Sensing Systems (RSS; Mears and Wentz 2016), NOAA’s Center for Satellite 
Applications and Research (STAR; Zou and Li 2014), and the University of Alabama in 
Huntsville (UAH; Christy et al. 2011). In particular, for the period 1979-2015, the rate of 
warming in the surface record was 0.256°C (0.460°F) per decade, versus trends of 0.223°C 
(0.40 1°F), 0.210°C (0.378°F), and 0.130 °C (0.234°F) per decade for RSS version 4, STAR 
version 3, and UAH version 6, respectively (after accounting for stratospheric influences). All 
trends are statistically significant. For the contiguous United States, the year 2015 was the 
second-warmest on record at the surface and the warmest on record for the middle troposphere. 
Generally speaking, surface and satellite records do not have identical trends because they do not 
represent the same physical quantity; surface measurements are made using thermometers in 
shelters about 1.5 meters (4-5 feet) above the ground whereas satellite measurements are mass- 
weighted averages of microwave emissions from deep atmospheric layers. The UAH record 
likely has a lower trend because it differs from the other satellite products in the treatment of 
target temperatures from the NOAA-9 satellite as well as in the correction for diurnal drift (Po- 
Chedley et al. 2015). 

Recent paleo-temperature evidence confirms the unusual character of wide-scale warming during 
the past few decades as determined from the instrumental record. The most important new 
paleoclimate study since NCA3 showed that for each of the seven continental regions, the 
reconstructed area- weighted average temperature for 1971-2000 was higher than for any other 
time in nearly 1,400 years (PAGES 2K 2013), although with significant uncertainty around the 
central estimate that leads to this conclusion. Recent (up to 2006) 30-year smoothed temperatures 
across temperate North America (including most of the continental United States) are similarly 
reconstructed as the warmest over the past 1,500 years (Trouet et al. 2013) (Figure 6.3). Unlike 
the PAGES 2k seven-continent result mentioned above, this conclusion for North America is 
robust in relation to the estimated uncertainty range. Reconstruction data since 1500 for western 
temperate North America show the same conclusion at the annual time scale for 1986-2005. 

This time period and the running 20-year periods thereafter are warmer than all possible 


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continuous 20-year sequences in a 1,000-member statistical reconstruction ensemble (Wahl and 
Smerdon 2012). 


[INSERT FIGURE 6.2 HERE: 

Figure 6.2. Pollen-based temperature reconstruction for temperate North America. Pollen-based 

temperature reconstruction for temperate North America. The blue curve depicts the pollen- 


based reconstruction of 30-year averages (as anomalies from 1904 to 1980) for the temperate 


region (30°-55°N, 7°5-130°W). The red curve shows the corresponding tree ring-based decadal 

average reconstruction, which was smoothed and used to calibrate the lower-frequency pollen 



based estimate. Light (medium) blue zones indicate 2 standard error (1 standard error) 


uncertainty estimations associated with each 30-year value. The black curve shows comparably 
smoothed instrumental temperature values up to 1980. The dashed black line represents the 
average temperature anomaly of comparably smoothed instrumental data for the period 2000- 
2006. (Figure source: NOAA/NCEI)] 

6.1.2. Temperature Extremes 

Shifts in temperature extremes are examined using a suite of societally relevant climate change 
indices (Zhang et al. 2011) derived from long-term observations of daily surface temperature 
(Menne et al. 2012). The coldest and warmest temperatures of the year are of particular 
relevance given their widespread use in engineering, agricultural, and other sectoral applications 
(for example, extreme annual design conditions by the American Society of Heating, 
Refrigeration, and Air Conditioning; plant hardiness zones by the U.S. Department of 
Agriculture). Cold and warm spells (that is, extended periods of below- or above-normal 
temperature) are likewise of great importance because of their numerous societal and 
environmental impacts, which span from human health to plant phenology. Changes are 
considered for a spectrum of event frequencies and intensities, ranging from the typical annual 
extreme to the l-in-10 year event (an extreme that only has a 10% chance of occurrence in any 
given year). Generally speaking, changes in many extremes have been larger than changes in 
average temperature. 

The coldest daily temperature of the year increased at most locations in the contiguous United 
States through 2015 (Figure 6.3). All regions experienced net increases (Table 6.2), with the 
largest rises in the Northern Great Plains and the Northwest (roughly 2.8°C [5.0°F]), and the 
smallest in the Southeast (about 0.6°C [1.0°F]). In general, there were increases throughout the 
period of record, with a slight acceleration in the past few decades (Figure 6.3). The temperature 
of extremely warm days (l-in-10 year events) generally exhibited the same pattern of increases 
as the coldest daily temperature of the year. Consistent with these increases, the number of cool 
nights per year (those with a minimum temperature below the 10th percentile) declined in all 
regions, with much of the West having decreases of roughly two weeks. 


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[INSERT TABLE 6.2 HERE: 


Table 6.2. Observed changes in temperature extremes (°F) for each NCA region. Changes are 

the difference between the average for present-day (1986-2015) and the average for the first half 

of the last century (1901-1960). 

]• 



[INSERT FIGURE 6.3 HERE: 



Figure 6.3. Observed changes in the coldest and warmest daily temperatures (°F) of the year. 


Maps (top) depict changes at stations; changes are the difference between the average for 
present-day (1986-2015) and the average for the first half of the last century (1901-1960). Time 
series (bottom) depict changes over the contiguous United States. (Figure source: 

NOAA/NCEI)]. 

The warmest daily temperature of the year generally increased at locations throughout the West 
(Figure 6.3), as did the temperature of extremely warm days (l-in-10 year events) and the 
number of warm days per year (those with a maximum temperature above the 90th percentile). 
The largest regional increases were in the Southwest (Table 6.2). In contrast, there were 
decreases in maximum temperatures in almost all locations east of the Rocky Mountains; the 
decreases were actually larger for the l-in-10 year events than for the warmest day of the year. 
The decreases in the eastern half of Nation (Figure 6.3) are generally tied to the hot summers in 
the 1930s Dust Bowl era, particularly across the Great Plains, where extreme agricultural 
drought and land mismanagement resulted in denuded landscapes, depleted soil moisture, and 
reduced evaporative cooling. Since the mid-1960s, however, there has been a slight increase in 
the warmest daily temperature of the year. 

The frequency of cold spells (brief periods of below-normal temperatures) has steadily fallen 
across the contiguous United States during the past century (Figure 6.4). The frequency of 
intense cold waves (l-in-5 year events) peaked in the 1980s in all regions (including Alaska) and 
then reached record-low levels in the 2000s (Peterson et al. 2013). Nationally, the average 
temperature of extreme cold waves (5-day, l-in-10 year events) was about 1.0 °C (1.8°F) warmer 
in the past three decades than in the first half of the 20th century, with increases in excess of 
1.7°C (3.0°F) in the Southwest, Northwest, and Northern Great Plains (Table 6.2). 

[INSERT FIGURE 6.4 HERE: 

Figure 6.4. Observed changes in cold and wann spells in the contiguous United States. The top 
panel depicts changes in the frequency of cold spells, the middle panel depicts changes in the 
frequency of warm spells, and the bottom panel depicts changes in the intensity of heat waves. 
(Figure source: NOAA/NCEI)] 

Warm spells (brief periods of above-normal temperatures) increased in frequency until the mid- 
1930s, became somewhat less common through the mid-1960s, and increased in frequency again 
thereafter (Figure 6.4). As with warm daily temperatures, the peak period for heat waves was the 
1930s in most regions except the West. Nationwide, the average temperature of extreme heat 


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waves (5-day, l-in-10 year events) was about 0.8°C (1.5°F) warmer in the first half of the 20th 
century than in the past three decades, with the Midwest having the largest regional difference 
(Table 6.2). The frequency of intense heat waves (l-in-5 year events) has generally increased 
since the 1960s in most regions except the Midwest and the Great Plains (Peterson et al. 2013; 
Smith et al. 2013). Since the early 1980s (Figure 6.4), there is suggestive evidence of a slight 
increase in the intensity of heat waves nationwide (Russo et al. 2014) as well as an increase in 
the concurrence of droughts and heat waves (Mazdiyasni and AghaKouchak 2015). Recent warm 
spells have generally been longer in duration than those in the 1930s, as evidenced by the multi- 
month heat waves in the Midwest in 2012. 

6.2 Detection and Attribution 

6.2.1 Average Temperatures 

While a confident attribution of global temperature increases to anthropogenic forcing has been 
made (Bindoff et al. 2013), detection and attribution assessment statements for smaller regions 
are generally much weaker. Nevertheless, some detectable anthropogenic influences on average 
temperature have been reported for North America and parts of the United States (e.g., Christidis 
et al. 2010; Bonfils et al. 2008; Pierce et al. 2009). Figure 6.5 shows an example for 1901-2015 
temperature trends, indicating a detectable anthropogenic warming since 1901 over the western 
and northern regions of the contiguous United States for the CMIP5 multi-model ensemble — a 
condition that was also met for most of the individual models (Knutson et al. 2013a). The 
Southeast stands out as the only region with no “detectable” warming since 1901; observed 
trends there were inconsistent with CMIP5 All Forcing historical runs (Knutson et al. 2013a). 

The cause of this “wanning hole,” or lack of a long-term wanning trend, remains uncertain, 
though it is likely a combination of natural and human causes. Some studies conclude that 
changes in anthropogenic aerosols have played a crucial role (e.g., Leibensperger et al. 2012a, b; 
Yu et al. 2014), whereas other studies infer a possible large role for internal climate variability 
(e.g., Meehl et al. 2012; Knutson et al. 2013a) as well as changes in land use (e.g., Goldstein et 
al. 2009; Xu et al. 2015). Notably, the Southeast has been warming rapidly since the early 1960s 
(Walsh et al. 2014; Pan et al. 2013). 


[INSERT FIGURE 6.5 HERE: 


Figure 6.5. Detection and attribution assessment of trends in average annual temperature (°F 

)• 

Grid-box values indicate whether trends for 1901-2015 are detectable (that is, distinct from 


natural variability) and/or consistent with CMIP5 historical All-Forcing runs. If the grid-box 

trend is found to be both detectable and either consistent with or greater than the warming in the 

All-Forcing runs, then the grid box is assessed as having a detectable anthropogenic contribution 
to warming over the period. (Figure source: updated from Knutson et al. 2013; © American 


Meteorological Society, used with permission)] 


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6.2.2 Temperature Extremes 

IPCC AR5 (Bindoff et al. 2013) concluded that it is very likely that human influence has 
contributed to the observed changes in frequency and intensity of temperature extremes on the 
global scale since the mid-20th century. The combined influence of anthropogenic and natural 
forcings was also detectable over large subregions of North America (e.g., Zwiers et al. 2011; 
Min et al. 2013). In general, however, results for the contiguous United States are not as 
compelling as for global land areas, in part because detection of changes in U.S. regional 
temperature extremes is affected by extreme temperature in the 1930s (Peterson et al. 2013). 
Table 6.3 summarizes available attribution statements for recent extreme U.S. temperature 
events. As an example, the recent record or near-record high March-May average temperatures 
occurring in 2012 over the eastern United States were attributed in part to external (natural plus 
anthropogenic) forcing (Knutson et al. 2013b); the century-scale trend response of temperature to 
external forcing is typically a close approximation to the anthropogenic forcing response alone. 
Another study found that although the extreme March 2012 warm anomalies over the United 
States were mostly due to natural variability, anthropogenic warming contributed to the severity 
(Dole et al. 2014). Such statements reveal that both natural and anthropogenic factors influence 
the severity of extreme temperature events. Nearly every modem analysis of current extreme hot 
and cold events reveals some degree of attributable human influence. 


[INSERT TABLE 6.3 HERE: 


Table 6.3. Extreme temperature events in the United States for which attribution statements have 

been made. There are three possible attribution statements: “+” shows an attributable human- 


induced increase in frequency or intensity, shows an attributable human-induced decrease in 


frequency or intensity, “0” shows no attributable human contribution.] 


6.3 Projected Changes 

6.3.1 Average Temperatures 

Temperature projections are based on global model results and associated downscaled products 
from CMIP5 using a suite of Representative Concentration Pathways (RCPs). In contrast to 
NCA3, model weighting is employed to refine projections of temperature for each RCP (Ch. 4: 
Projections; Appendix B: Model Weighting). Weighting parameters are based on model 
independence and model skill over North America for seasonal temperature and annual extremes 
(Figure 6.5). Unless stated otherwise, all changes presented here represent the weighted multi- 
model mean. The weighting scheme helps refine confidence and likelihood statements, but 
projections of U.S. surface air temperature remain very similar to those in NCA3. Generally 
speaking, extreme temperatures are projected to increase at a greater rate than changes in average 
temperatures (Collins et al. 2013). 

[INSERT FIGURE 6.6 HERE: 

Figure 6.6. Relative performance of the CMIP5 models used in this study in simulating observed 


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North American temperature indices. The first four rows depict performance for extremes while 

the next four rows depict performance for seasonal averages. The last row depicts the combined 

performance for all metrics. Models are ordered from left (best) to worst (right) based upon this 

combined metric. (Figure source: adapted from Sanderson et ah, submitted 2016)] 


The average annual temperature of the contiguous United States is projected to rise throughout 
the century. Near-term increases will be about 1.4°C (2.5°F) for RCP4.5 and 1.6°C (2.9°F) for 
RCP8.5; the similarity in warming reflects the similarity in greenhouse gas concentrations during 
this period (Figure 4.1). Notably, a 1.4°C (2.5°F) increase makes the near-term average roughly 
comparable to the hottest year in the historical record (2012). In other words, recent record- 
breaking years could be “normal” by about 2030. By late-century, the RCPs diverge in a 
statistically significant sense, leading to very different rates of warming: approximately 2.8°C 
(5.0°F) for RCP4.5 and 4.8°C (8.7°F) for RCP8.5. Unforced internal variations will continue to 
be evident in future U.S. temperatures, particularly in the near-term. Slightly larger increases are 
projected for summer than winter (except for Alaska), and average maximum temperature will 
rise slightly faster than average minimum temperature. 

Warming is projected for all parts of the United States by mid- and late-century (Figure 6.7). The 
largest changes are in Alaska (5.5°C [10°F] or more by late-century under RCP8.5), in part due 
to decreases in surface albedo as snow cover declines. In the contiguous United States, northern 
regions (the Northeast, Midwest, and Northern Great Plains) have slightly more warming than 
elsewhere, consistent with polar amplification (Table 6.4). The Southeast has slightly less 
warming because anthropogenic effects are partially offset by latent heat release from increases 
in evapotranspiration (as is already evident in the observed record). From a sub-regional 
perspective, less warming is projected along the coasts due to the moderating effects of the 
ocean, although the temperature increases are still substantial. In addition, anthropogenic 
warming at higher elevations may be underestimated because the resolution of the CMIP5 
models does not capture orography in detail, with important implications for future snowpack in 
the mountainous West. 

[INSERT FIGURE 6.7 HERE: 

Figure 6.7. Projected changes in average annual temperature (°F) for mid- and late-21st century. 
Changes are the difference between the average for mid-century (2036-2065; top) or late-century 
(207 1-2100, bottom) and the average for near-present (1976-2005). (Figure source: CICS-NC / 
NOAA/NCEI).] 

[INSERT TABLE 6.4 HERE: 

Table 6.4. Projected changes in average annual temperature (°F) for each NCA region. Changes 
are the difference between the average in the future (either mid- or late-century) and the average 

for near-present (1976-2005).] 


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6.3.2 Temperature Extremes 

The coldest and warmest daily temperatures of the year are projected to increase substantially 
over the coming decades (Figure 6.8). Under RCP8.5, increases in most areas exceed 2.8°C [5°F] 
by mid-century (Fischer et al. 2013), rising to 5.5°C [10°F] or more by late-century (Sillmann et 
al. 2013). From a regional perspective, the coldest temperatures will increase the most in Alaska 
and in the northern half of the contiguous United States whereas the wannest temperatures will 
exhibit somewhat more unifonn changes geographically (Table 6.5). Changes in “very rare” 
temperature extremes are particularly dramatic under RCP8.5; by late century, current 1 -in-20 
year maximums are projected to occur every year, while current 1 -in-20 year minimums are not 
projected to occur at all over the contiguous United States (Wuebbles et al. 2014). Finally, there 
is a substantial projected decrease in the number of days with a minimum temperature below 
freezing, particularly in the West, and a marked increase in the number days with a maximum 
over 38°C [100°F], particularly in the Southwest, Great Plains, and Southeast (Figure 6.9). 

[INSERT FIGURE 6.8 HERE: 

Figure 6.8. Projected changes in the coldest and warmest daily temperatures (°F) of the year. 
Changes are the difference between the average for mid-century (2036-2065) and the average 
for near-present (1976-2005) under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)] 

[INSERT FIGURE 6.9 HERE: 

Figure 6.9. Projected changes in the number of days per year with a minimum temperature 
below 32°F (left) and a maximum temperature above 100°F (right). Changes are the difference 
between the average for mid-century (2036-2065) and the average for near-present (1976-2005) 
under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI)] 

[INSERT TABLE 6.5 HERE: 

Table 6.5. Projected changes in temperature extremes (°F) for each NCA region. Changes are 
the difference between the average for mid-century and the average for near-present (1976- 
2005) under RCP8. 5.] 

The frequency and intensity of cold spells is projected to decrease while the frequency and 
intensity of warm spells is projected to increase throughout the century. The frequency of cold 
spells will decrease the most in Alaska and the least in the Northeast while the frequency of 
wann spells will increase in all regions, particularly the Southeast, Southwest, and Alaska. By 
mid-century, decreases in the frequency of cold spells are similar across RCPs whereas increases 
in the frequency of warm spells are about 50% greater in RCP8.5 than RCP4.5 (Sun et al. 2015). 
The intensity of cold waves and heat waves is also projected to increase dramatically, 
particularly under RCP8.5. By mid-century, both extreme cold waves and extreme heat waves 
(5-day, 1 -in- 1 0 year events) are projected to have temperature increases of at least 6°C [1 1°F] 
nationwide, with larger increases in northern regions (the Northeast, Midwest, Northern Great 
Plains, and Northwest; Table 6.5). Changes in land surface properties will play an important role 


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Chapter 6 


1 in the changes in wann spells. In the stagnant air conditions associated with prolonged heat 

2 waves, soils will dry out faster in a warmer climate. The reduction in evaporative cooling then 

3 compounds the average warming during the heat wave. 

4 


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Chapter 6 


1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 The annual-average, near-surface air temperature over the contiguous United States has 

4 increased by about 1.2°F (0.7°C) between 1901 and 2015. Surface and satellite data both show 

5 rapid warming since the late 1970s, while paleo-temperature evidence shows that recent decades 

6 have been the warmest in at least the past 1,500 years. (. Extremely likely, High confidence) 

7 Description of Evidence Base 

8 The key finding and supporting text summarize extensive evidence documented in the climate 

9 science literature. Similar statements about changes have also been made in other national 

10 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

1 1 SAP 1.1: Temperature trends in the lower atmosphere, and SAP 1.6: Global Climate Change 

12 Impacts in the United States). Statements about annual events and extremes are documented in 

13 the State of the Climate Reports by the Bulletin of the American Meteorological Society. 

14 Evidence for changes in U.S. climate arises from multiple analyses of data from in-situ, satellite, 

15 and other records undertaken by many groups over several decades. The primary dataset for 

16 surface temperatures in the contiguous United States is nClimGrid (Vose et al. 2014), with other 

17 recently released datasets for Alaska and other areas. Changes in sea surface temperatures are 

18 derived from the NOAA Global Temperature dataset (Vose et al. 2012), which now uses the 

19 Extended Reconstructed Sea Surface Temperature Dataset version 4 (Huang et al. 2015). Several 

20 recently improved satellite datasets document changes in middle tropospheric temperatures 

21 (Mears and Wentz 2016; Zou and Li 2016; Christy et al. 2011). Longer-term changes are 

22 depicted using multiple paleo analyses (e.g., Wahl and Smerdon 2012, Truet et al. 2013). 

23 Major Uncertainties 

24 The primary uncertainties for surface data relate to historical changes in station location, 

25 temperature instrumentation, observing practice, and spatial sampling. Satellite records are 

26 similarly impacted by non-climatic changes such as orbital decay, diurnal sampling, and 

27 instrument calibration to target temperatures. Several uncertainties are inherent in temperature- 

28 sensitive proxies, such as dating techniques and spatial sampling. 

29 Assessment of Confidence 

30 Very High 

3 1 Likelihood of Impact 

32 Extremely Likely 

33 Summary Sentence 

34 There is very high confidence in observed changes in average temperature over the United States 

35 based upon the convergence of evidence from multiple data sources, analyses, and assessments. 


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Chapter 6 


1 Key Finding 2 

2 Accompanying the rise is average temperatures, there have been - as is to be expected - 

3 increases in extreme temperature events in most parts of the United States. Since the early 1900s, 

4 the temperature of extremely cold days has increased throughout the contiguous United States, 

5 and the temperature of extremely wann days has increased across much of the West. In recent 

6 decades, intense cold waves have become less common while intense heat waves have become 

7 more common. (. Extremely likely, Very high confidence ) 

8 Description of Evidence Base 

9 The key finding and supporting text summarize extensive evidence documented in the climate 

10 science literature. Similar statements about changes have also been made in other national 

1 1 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

12 SAP3.3: Weather and Climate Extremes in a Changing Climate) and the IPCC Special Report on 

13 Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. 

14 Statements about annual events and extremes are documented in the State of the Climate Reports 

15 and the Explaining Extreme Events Reports by the Bulletin of the American Meteorological 

16 Society. 

17 Evidence for changes in U.S. climate arises from multiple analyses of in situ data and 

18 atmospheric reanalyses using widely published climate extremes indices. The primary source of 

19 in situ data is the Global Historical Climatology Network - Daily dataset (Menne et al. 2011), 

20 the largest collection of U.S. and global temperature data in the world. Climate extremes indices, 

21 comprehensively documented in Zhang et al. 2011, have been employed in numerous 

22 publications and assessments. 

23 Major Uncertainties 

24 The primary uncertainties for in situ data relate to historical changes in station location, 

25 temperature instrumentation, observing practice, and spatial sampling (particularly the precision 

26 of estimates of change in areas and periods with low station density, such as the intermountain 

27 West in the early 20th century). 

28 Assessment of Confidence 

29 Very High 

30 Likelihood of Impact 

3 1 Extremely Likely 

32 Summary Sentence 

33 There is very high confidence in observed changes in temperature extremes over the United 

34 States based upon the convergence of evidence from multiple data sources, analyses, and 

35 assessments. 

36 


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1 Key Finding 3 

2 The average annual temperature of the contiguous United States is projected to rise throughout 

3 the century. Increases of at least 2.5°F (1.4°C) are projected over the next few decades, meaning 

4 that recent record-setting years will be relatively “common” in the near future. Increases of 5.0°- 

5 7.5°F (2.8°-4.8°C) are projected by late century depending upon the level of future emissions. 

6 (. Extremely likely, Very high confidence) 

7 Description of Evidence Base 

8 The key finding and supporting text summarize extensive evidence documented in the climate 

9 science literature. Similar statements about changes have also been made in other national 

10 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

1 1 SAP 1.6: Global Climate Change Impacts in the United States). The basic physics underlying the 

12 impact of human emissions on global climate has also been documented in every IPCC 

13 assessment. 

14 Major Uncertainties 

15 Global climate models are subject to structural and parametric uncertainty, resulting in a range of 

16 estimates of future changes in average temperature. This is partially mitigated through the use of 

17 model weighting and pattern scaling. Furthennore, virtually every ensemble member of every 

18 model projection contains an increase in temperature by mid- and late-century. Empirical 

19 downscaling introduces additional uncertainty (e.g., with respect to stationarity). Projections will 

20 improve in the future along with improvements in model physics and resolution. 

21 Assessment of Confidence 

22 Very High 

23 Likelihood of Impact 

24 Extremely likely 

25 Summary Sentence 

26 There is high confidence in projected changes in average temperature over the United States 

27 based upon the convergence of evidence from multiple model simulations, analyses, and 

28 assessments. 

29 

30 


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Chapter 6 


1 Key Finding 4 

2 Extreme temperatures are projected to increase even more than average temperatures. The 

3 temperatures of extremely cold days and extremely wann days are both projected to increase. 

4 Cold waves are projected to become less intense while heat waves will become more intense. 

5 (. Extremely likely, Very high confidence) 

6 Description of Evidence Base 

7 The key finding and supporting text summarize extensive evidence documented in the climate 

8 science literature. Similar statements about changes have also been made in other national 

9 assessments (such as NCA3) and in reports by the Climate Change Science Program (such as 

10 SAP 3,3: Weather and Climate Extremes in a Changing Climate). The basic physics underlying 

1 1 the impact of human emissions on global climate has also been documented in every IPCC 

12 assessment. 

13 Major Uncertainties 

14 Global climate models are subject to structural and parametric uncertainty, resulting in a range of 

15 estimates of future changes in temperature extremes. This is partially mitigated through the use 

16 of model weighting and pattern scaling. Furthennore, virtually every ensemble member of every 

17 model projection contains an increase in temperature by mid- and late-century. Empirical 

18 downscaling introduces additional uncertainty (e.g., with respect to stationarity). Projections will 

19 improve in the future along with improvements in model physics and resolution. 

20 Assessment of Confidence 

21 Very High 

22 Likelihood of Impact 

23 Extremely likely 

24 Summary Sentence 

25 There is high confidence in projected changes in temperature extremes over the United States 

26 based upon the convergence of evidence from multiple model simulations, analyses, and 

27 assessments. 

28 


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Chapter 6 


1 TABLES 

2 Table 6.1. Observed changes in average annual temperature (°F) for each NCA region. Changes 

3 are the difference between the average for present-day (1986-2015) and the average for the first 

4 half of the last century (1901-1960). 


NCA Region 

Average Annual 
Temperature 

Average Annual 
Maximum 
Temperature 

Average Annual 
Minimum 
Temperature 

Contiguous U.S. 

1.18 

1.00 

1.35 

Northeast 

1.37 

1.09 

1.65 

Southeast 

0.40 

0.10 

0.70 

Midwest 

1.18 

0.71 

1.66 

Great Plains North 

1.62 

1.59 

1.65 

Great Plains South 

0.70 

0.50 

0.90 

Southwest 

1.56 

1.57 

1.56 

Northwest 

1.51 

1.48 

1.52 

Alaska 

1.52 

1.29 

1.76 

Hawaii 

0.75 

- 

- 


5 


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1 Table 6.2. Observed changes in temperature extremes (°F) for each NCA region. Changes are 

2 the difference between the average for present-day (1986-2015) and the average for the first half 

3 of the last century (1901-1960). 


NCA Region 

Coldest Day of 
the Year 

Coldest 5-Day 
l-in-10 Year 

Event 

Warmest Day 
of the Year 

Warmest 5-Day 
l-in-10 Year 

Event 

Northeast 

3.04 

1.13 

-0.99 

-1.85 

Southeast 

1.13 

0.43 

-1.53 

-1.64 

Midwest 

3.04 

-1.32 

-2.26 

-4.12 

Great Plains North 

4.80 

3.16 

-1.16 

-1.45 

Great Plains South 

3.44 

1.55 

-1.16 

-1.10 

Southwest 

4.12 

3.31 

0.40 

0.14 

Northwest 

5.00 

3.53 

-0.22 

-0.85 


4 


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1 Table 6.3. Extreme temperature events in the United States for which attribution statements have 

2 been made. There are three possible attribution statements: “+” shows an attributable human- 

3 induced increase in frequency or intensity, shows an attributable human-induced decrease in 

4 frequency or intensity, “0” shows no attributable human contribution. 


Study 

Period 

Region 

Type 

Statement 

Rupp et al. 2012 

Angelil et al. 2016 

Spring/Summer 

2011 

Texas 

Hot 

+ 

+ 

Hoerling et al. 2013 

Summer 2011 

Texas 

Hot 

+ 

Diffenbaugh and Scherer 2013 

Angelil et al. 2016 

July 2012 

Northcentral and 
Northeast 

Hot 

+ 

+ 

Cattiaux and Yiou 2013 

Angelil et al. 2016 

Spring 2012 

East. 

Hot 

0 

+ 

Knutson et al. 2013 

Angelil et al. 2016 

Spring 2012 

East 

Hot 

+ 

+ 

Jeon et al 2016 

Summer 2011 

T exas/ Oklahoma 

Hot 

+ 

Dole et al. 2014 

March 2012 

Upper Midwest 

Hot 

+ 

Seager et al. 2014 

2011-2014 

California 

Hot 

+ 

Wolter et al. 2015 

Winter 2014 

Midwest 

Cold 

- 

Trenary et al. 2015 

Winter 2014 

East 

Cold 

0 


5 

6 


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1 Table 6.4. Projected changes in average annual temperature (°F) for each NCA region. Changes 

2 are the difference between the average in the future (either mid- or late-century) and the average 

3 for near-present (1976-2005). 


NCA Region 

RCP4.5 

Mid-Century 

(2036-2065) 

RCP 8.5 
Mid-Century 
(2036-2065) 

RCP 4.5 
Late-Century 
(2071-2100) 

RCP 8.5 
Late-Century 
(2071-2100) 

Contiguous U.S. 

3.79 

4.83 

5.03 

8.72 

Northeast 

3.98 

5.09 

5.27 

9.11 

Southeast 

3.40 

4.30 

4.43 

7.72 

Midwest 

4.21 

5.29 

5.57 

9.49 

Great Plains 
North 

4.05 

5.10 

5.44 

9.37 

Great Plains 
South 

3.62 

4.61 

4.78 

8.44 

Southwest 

3.72 

4.80 

4.93 

8.65 

Northwest 

3.66 

4.67 

4.99 

8.51 


4 

5 


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1 Table 6.5. Projected changes in temperature extremes (°F) for each NCA region. Changes are the 

2 difference between the average for mid-century and the average for near-present (1976-2005) 

3 underRCP8.5. 


NCA Region 

Coldest Day of 
the Year 

Coldest 5-Day 
l-in-10 Year 

Event 

Warmest Day 
of the Year 

Warmest 5-Day 
l-in-10 Year 

Event 

Northeast 

9.51 

15.93 

6.51 

12.88 

Southeast 

4.97 

8.84 

5.79 

11.09 

Midwest 

9.44 

15.52 

6.71 

13.02 

Great Plains North 

8.01 

12.01 

6.48 

12.00 

Great Plains South 

5.49 

9.41 

5.70 

10.73 

Southwest 

6.13 

10.20 

5.85 

11.17 

Northwest 

7.33 

10.95 

6.25 

12.31 


4 


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Chapter 6 


1 FIGURES 


Annual Temperature 



3 

4 Figure 6.1. Observed changes in annual, winter, and summer temperature (°F). Changes are the 

5 difference between the average for present-day (1986-2015) and the average for the first half of 

6 the last century (1901-1960 for the contiguous United States, 1925-1960 for Alaska and 

7 Hawaifi). (Figure source: NOAA/NCEI) 


236 


1 

2 

3 

4 

5 

6 

7 

8 

9 

10 


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Chapter 6 



Figure 6.2. Pollen-based temperature reconstruction for temperate North America. The blue 
curve depicts the pollen-based reconstruction of 30-year averages (as anomalies from 1904 to 
1980) for the temperate region (30°-55°N, 7°5-130°W). The red curve shows the corresponding 
tree ring-based decadal average reconstruction, which was smoothed and used to calibrate the 
lower- frequency pollen-based estimate. Light (medium) blue zones indicate 2 standard error ( 1 
standard error) uncertainty estimations associated with each 30-year value. The black curve 
shows comparably smoothed instrumental temperature values up to 1980. The dashed black line 
represents the average temperature anomaly of comparably smoothed instrumental data for the 
period 2000-2006. (Figure source: NOAA/NCEI) 


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Chapter 6 


Change in Coldest Temperature of the Year 

1 986-20 1 5 Average Minus 1901-1960 Average 



Change in Warmest Temperature of the Year 

1986-2015 Average Minus 1901-1960 Average 





Difference (*F> 

• <•« 


• -4 to *2 
•2100 
0k>2 

• 2K>4 

• 4 K>6 

• >e 


1 

2 

3 

4 

5 




Figure 6.3. Observed changes in the coldest and warmest daily temperatures (°F) of the year. 
Maps (top) depict changes at stations; changes are the difference between the average for 
present-day (1986-2015) and the average for the first half of the last century (1901-1960). Time 
series (bottom) depict changes over the contiguous United States. (Figure source: NOAA/NCEI) 


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Chapter 6 





3 Figure 6.4. Observed changes in cold and warm spells in the contiguous United States. The top 

4 panel depicts changes in the frequency of cold spells, the middle panel depicts changes in the 

5 frequency of warm spells, and the bottom panel depicts changes in the intensity of heat waves. 

6 (Figure source: NOAA/NCEI) 


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Chapter 6 


1 

2 

3 

4 

5 

6 

7 

8 


Assessment of Annual Surface Temperature Trends (1901-2015) 



a) Observed trend (1901-201 



b) CMIP5 ensemble trend (1901-2015) 


c) Summary trend assessment 



Insufficient data 



H -1 - 5 


r 

V ' 4 


Detectable anthro. increase, 
greater than modeled 

Detectable anthro. increase, 
consistent with models 


Detectable increase, 
less than modeled 


No detectable trend; white 
hatching: consistent with models 


White hatching: 

Obs. Consistent with All-Forcing Simulations 


Figure 6.5. Detection and attribution assessment of trends in average annual temperature (°F). 
Grid-box values indicate whether trends for 1901-2015 are detectable (that is, distinct from 
natural variability) and/or consistent with CMIP5 historical All-Forcing runs. If the grid-box 
trend is found to be both detectable and either consistent with or greater than the warming in the 
All-Forcing runs, then the grid box is assessed as having a detectable anthropogenic contribution 
to warming over the period. (Figure source: updated from Knutson et al. 2013; © American 
Meteorological Society. Used with permission.) 


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Chapter 6 


1 

2 

3 

4 

5 

6 
7 


North America Temperature Skill 


txx (hot days) 
txn (cool days) 
tnn (cold nights) 
tnx (warm nights) 
Mean TAS DJF 
Mean TAS MAM 
Mean TAS JJAb 
Mean TAS SON 
Mean TAS ALL 


— i — i — i — i — i — i — i — i — i — i — i — i — i — r™n — i — i — i — tmh — i — i — i — i — i — n 

f'N lfl ! 


I 

I 


I 





O > C\J c\J 

o CM W CO 2 

I ^ (/) pL LU c/3 

^ C/5 _J , J LU 
^ LU < I £ C 

0 pj' § CO CL 3 


i o 


o 

o 

CL 


Figure 6.6. Relative performance of the CMIP5 models used in this study in simulating observed 
North American temperature indices. The first four rows depict performance for extremes while 
the next four rows depict performance for seasonal averages. The last row depicts the combined 
performance for all metrics. Models are ordered from left (best) to worst (right) based upon this 
combined metric. (Figure source: adapted from Sanderson et ah, submitted 2016) 


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Chapter 6 


Projected Changes in Average Annual Temperature 


Mid 21st Century, Lower Emissions Mid 21st Century, Higher Emissions 



Late 21st Century, Lower Emissions Late 21st Century, Higher Emissions 



1 

2 

3 Figure 6.7. Projected changes in average annual temperature (°F) for mid- and late-21st century. 

4 Changes are the difference between the average for mid-century (2036-2065; top) or late-century 

5 (2071-2100, bottom) and the average for near-present (1976-2005). (Figure source: CICS-NC / 

6 NOAA/NCEI) 


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Projected Change in Coldest Temperature of the Year (°F) Projected Change in Warmest Temperature of the Year (°F) 



1 

2 Figure 6.8. Projected changes in the coldest and warmest daily temperatures (°F) of the year. 

3 Changes are the difference between the average for mid-century (2036-2065) and the average 

4 for near-present (1976-2005) under RCP8.5. (Figure source: CICS-NC / NOAA/NCEI) 

5 


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Chapter 6 


1 

2 

3 

4 

5 

6 


Projected Change in Number of Days with 
Minimum Temperature < 32°F 


Projected Change in Number of Days with 
Maximum Temperature > 100°F 






Figure 6.9. Projected changes in the number of days per year with a minimum temperature below 
32°F (left) and a maximum temperature above 100°F (right). Changes are the difference between 
the average for mid-century (2036-2065) and the average for near-present (1976-2005) under 
RCP8.5. (Figure source: CICS-NC / NOAA/NCEI) 


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Chapter 6 


1 REFERENCES 

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3 independent assessment of anthropogenic attribution statements for recent extreme 

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7 Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, and X. Zhang, 2013: 

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34 York, NY, USA, 1029-1136. http://dx.doi.org/10.1017/CB09781107415324.024 

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Chapter 6 


1 Diffenbaugh, N.S. and M. Scherer, 2013: Likelihood of July 2012 U.S. temperatures in pre- 

2 industrial and current forcing regimes [in "Explaining Extremes of 2012 from a Climate 

3 Perspective"] . Bulletin of the American Meteorological Society, 94 , S6-S9. 

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6 Murray, M. Chen, K. Wolter, and T. Zhang, 2014: The Making of an Extreme Event: Putting 

7 the Pieces Together. Bulletin of the American Meteorological Society, 95 , 427-440. 

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9 Fischer, E.M., U. Beyerle, and R. Knutti, 2013: Robust spatially aggregated projections of 

10 climate extremes. Nature Climate Change, 3 , 1033-1038. 

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12 Goldstein, A.H., C.D. Koven, C.L. Heald, and I.Y. Fung, 2009: Biogenic carbon and 

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32 Jones, G.S., P.A. Stott, and N. Christidis, 2013: Attribution of observed historical near surface 

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2 Surface Temperature Trends: CMIP3 and CMIP5 Twentieth-Century Simulations. Journal of 

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4 Knutson, T.R., F. Zeng, and A.T. Wittenberg, 2013b: The extreme March-May 2012 warm 

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12 Leibensperger, E.M., LJ. Mickley, DJ. Jacob, W.T. Chen, J.H. Seinfeld, A. Nenes, P.J. Adams, 

13 D.G. Streets, N. Kumar, and D. Rind, 2012: Climatic effects of 1950&ndash;2050 changes in 

14 US anthropogenic aerosols &ndash; Part 2: Climate response. Atmospheric Chemistry and 

15 Physics, 12 , 3349-3362. http://dx.doi.org/10.5194/acp-12-3349-2012 

16 Mazdiyasni, O. and A. AghaKouchak, 2015: Substantial increase in concurrent droughts and 

17 heatwaves in the United States. Proceedings of the National Academy of Sciences, 112 , 

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19 Mears, C.A. and F.J. Wentz, 2016?: Sensitivity of satellite-derived tropospheric temperature 

20 trends to the diurnal cycle adjustment. Journal of Climate, 29, 3629-3646. 

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22 Meehl, G.A., J.M. Arblaster, and G. Branstator, 2012: Mechanisms contributing to the warming 

23 hole and the consequent US east-west differential of heat extremes. Journal of Climate, 25 , 

24 6394-6408. http://dx.doi.org/10.1175/JCLI-D-ll-00655T 

25 Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the 

26 global historical climatology network-daily database. Journal of Atmospheric and Oceanic 

27 Technology, 29 , 897-910. http://dx.doi.org/10.1175/JTECH-D-ll-00103T 

28 Min, S.-K., X. Zhang, F. Zwiers, H. Shiogama, Y.-S. Tung, and M. Wehner, 2013: Multimodel 

29 Detection and Attribution of Extreme Temperature Changes. Journal of Climate, 26 , 7430- 

30 7451. http://dx.doi.org/10.1175/JCLI-D-12-00551T 

3 1 PAGES 2K, 2013: Continental- scale temperature variability during the past two millennia. 

32 Nature Geoscience, 6, 339-346. http://dx.doi.org/10.1038/ngeol797 

33 Pan, Z., X. Liu, S. Kumar, Z. Gao, and J. Kinter, 2013: Intermodel Variability and Mechanism 

34 Attribution of Central and Southeastern U.S. Anomalous Cooling in the Twentieth Century 


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1 as Simulated by CMIP5 Models. Journal of Climate, 26 , 6215-6237. 

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4 Peterson, T.C., R.R. Heim, R. Hirsch, D.P. Kaiser, H. Brooks, N.S. Diffenbaugh, R.M. Dole, J.P. 

5 Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D. Lettenmaier, G J. McCabe, 

6 C J. Paciorek, K.R. Ryberg, S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia, G. 

7 Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. 

8 Wuebbles, 2013: Monitoring and understanding changes in heat waves, cold waves, floods 

9 and droughts in the United States: State of knowledge. Bulletin of the American 

10 Meteorological Society, 94 , 821-834. http://dx.doi.Org/10.1175/BAMS-D-12-00066.l 

1 1 Pierce, D.W., T.P. Barnett, B.D. Santer, and P.J. Gleckler, 2009: Selecting global climate models 

12 for regional climate change studies. Proceedings of the National Academy of Sciences, 106 , 

13 8441 - 8446 . http ://dx .doi .org / 1 0 . 1 07 3/pnas .0900094 106 

14 Po-Chedley, S., TJ. Thorsen, and Q. Fu, 2015: Removing diurnal cycle contamination in 

15 satellite-derived tropospheric temperatures: Understanding tropical tropospheric trend 

16 discrepancies. Journal of Climate, 28, 2274-2290. http://dx.doi.Org/10.l 175/JCFI-D-13- 

17 00767.1 

18 Rupp, D.E., P.W. Mote, N. Massey, C .J. Rye, R. Jones, and M.R. Allen, 2012: Did human 

19 influence on climate make the 2011 Texas drought more probable? [in Explaining extreme 

20 events of 201 1 from a climate perspective] . Bulletin of the American Meteorological Society, 

21 93, 1052-1054. http://dx.doi.org/10.1175/BAMS-D-12-0002Ll 

22 Russo, S., A. Dosio, R.G. Graversen, J. Sillmann, H. Carrao, M.B. Dunbar, A. Singleton, P. 

23 Montagna, P. Barbola, and J.V. Vogt, 2014: Magnitude of extreme heat waves in present 

24 climate and their projection in a warming world. Journal of Geophysical Research: 

25 Atmospheres , 119 , 12,500-12,512. http://dx.doi.org/10.1002/2014JD022098 

26 Seager, R., M. Hoerling, D.S. Siegfried, h. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

27 Henderson, 2014: Causes and predictability of the 2011-14 California drought. 40 pp. NOAA 

28 Drought Task Force Narrative 

29 Team. http://docs.lib.noaa.gov/noaa_documents/OAR/CPO/MAPP/california_drought_201 1- 

30 2014.pdf 

3 1 Sillmann, J., V.V. Kharin, F.W. Zwiers, X. Zhang, and D. Bronaugh, 2013: Climate extremes 

32 indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of 

33 Geophysical Research: Atmospheres, 118 , 2473-2493. http://dx.doi.org/10.1002/jgrd.50188 


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1 Smith, T.T., B.F. Zaitchik, and J.M. Gohlke, 2013: Heat waves in the United States: Definitions, 

2 patterns and trends. Climatic Change, 118 , 811-825. http://dx.doi.org/10.1007/sl0584-012- 

3 0659-2 

4 Sun, L., K.E. Kunkel, L.E. Stevens, A. Buddenberg, J.G. Dobson, and D.R. Easterling, 2015: 

5 Regional Surface Climate Conditions in CMIP3 and CMIP5 for the United States: 

6 Differences, Similarities, and Implications for the U.S. National Climate Assessment. NOAA 

7 Technical Report NESDIS 144, 111 pp. National Oceanic and Atmospheric Administration, 

8 National Environmental Satellite, Data, and Information Service. 

9 http://www.nesdis.noaa.gov/technical reports/NOAA NESDIS Technical Report 144.pdf 

10 Thorne, P.W., M.G. Donat, R J.H. Dunn, C.N. Williams, L.V. Alexander, J. Caesar, I. Durre, I. 

1 1 Harris, Z. Hausfather, P.D. Jones, MJ. Menne, R. Rohde, R.S. Vose, R. Davy, A.M.G. 

12 Klein-Tank, J.H. Lawrimore, T.C. Peterson, and J.J. Rennie, 2016: Reassessing changes in 

13 diurnal temperature range: Intercomparison and evaluation of existing global data set 

14 estimates. Journal of Geophysical Research: Atmospheres , 121 , 5138-5158. 

1 5 http ://dx .doi .org/ 10.1 002/20 1 5 JD0245 84 

16 Trenary, L., T. DelSole, B . Doty, and M.K. Tippett, 2015: Was the Cold Eastern Us Winter of 

17 2014 Due to Increased Variability? Bulletin of the American Meteorological Society, 96 , 

18 S15-S19. http://dx.doi.org/10.1175/bams-d-15-00138T 

19 Trouet, V., H.F. Diaz, E.R. Wahl, A.E. Viau, R. Graham, N. Graham, and E.R. Cook, 2013: A 

20 1500-year reconstruction of annual mean temperature for temperate North America on 

21 decadal-to-multidecadal time scales. Environmental Research Letters, 8, 024008. 

22 http://dx.doi.Org/10.1088/1748-9326/8/2/024008 

23 Vose, R.S., S. Applequist, M. Squires, I. Durre, MJ. Menne, C.N. Williams, C. Fenimore, K. 

24 Gleason, and D. Arndt, 2016: Improved historical temperature and precipitation time series 

25 for Alaska climate divisions. Journal of Applied Meteorology and Climatology Submitted. 

26 Vose, R.S., S. Applequist, M. Squires, I. Durre, MJ. Menne, C.N.W. Jr., C. Fenimore, K. 

27 Gleason, and D. Arndt, 2014: Improved Historical Temperature and Precipitation Time 

28 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology , 53 , 

29 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248J 

30 Vose, R.S., D. Amdt, V.F. Banzon, D.R. Easterling, B. Gleason, B. Huang, E. Kearns, J.H. 

3 1 Lawrimore, M J. Menne, T.C. Peterson, R.W. Reynolds, T.M. Smith, C.N. Williams, and 

32 D.L. Wuertz, 2012: NOAA’s Merged Land-Ocean Surface Temperature Analysis. Bulletin of 

33 the American Meteorological Society, 93, 1677-1685. http://dx.doi.org/10J 175/BAMS-D- 

34 11-00241.1 


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1 Wahl, E.R. and J.E. Smerdon, 2012: Comparative performance of paleoclimate field and index 

2 reconstructions derived from climate proxies and noise-only predictors. Geophysical 

3 Research Letters, 39 , L06703. http://dx.doi.org/10.1029/2012GL051086 

4 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

5 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

6 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

7 Climate Change Impacts in the United States: The Third National Climate Assessment. 

8 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S . Global Change Research Program, 

9 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

10 Wolter, K„ J.K. Eischeid, X.-W. Quan, T.N. Chase, M. Hoerling, R.M. Dole, G.J.V. 

1 1 Oldenborgh, and J.E. Walsh, 2015: How Unusual was the Cold Winter of 2013/14 in the 

12 Upper Midwest? Bulletin of the American Meteorological Society, 96, S10-S14. 

1 3 http ://dx .doi .org / 10.117 5/bams-d- 1 5 -00 126.1 

14 Wuebbles, D., G. Meehl, K. Hayhoe, T.R. Karl, K. Kunkel, B. Santer, M. Wehner, B. Colle, 

15 E.M. Fischer, R. Fu, A. Goodman, E. Janssen, V. Kharin, H. Lee, W. Li, L.N. Long, S.C. 

16 Olsen, Z. Pan, A. Seth, J. Sheffield, and L. Sun, 2014: CMIP5 Climate Model Analyses: 

17 Climate Extremes in the United States. Bulletin of the American Meteorological Society, 95 , 

18 571-583. http://dx.doi.org/10.1175/BAMS-D-12-00172T 

19 Xu, L., H. Guo, C.M. Boyd, M. Klein, A. Bougiatioti, K.M. Cerully, J.R. Hite, G. Isaacman- 

20 VanWertz, N.M. Kreisberg, C. Knote, K. Olson, A. Koss, A.H. Goldstein, S.V. Hering, J. de 

21 Gouw, K. Baumann, S.-H. Lee, A. Nenes, R.J. Weber, and N.L. Ng, 2015: Effects of 

22 anthropogenic emissions on aerosol formation from isoprene and monoterpenes in the 

23 southeastern United States. Proceedings of the National Academy of Sciences, 112 , 37-42. 

24 http://dx.doi.org/10.1073/pnas.14176091 12 http://www.pnas.Org/content/l 12/1/37 .abstract 

25 Yu, S., K. Alapaty, R. Mathur, J. Pleim, Y. Zhang, C. Nolte, B. Eder, K. Foley, and T. 

26 Nagashima, 2014: Attribution of the United States “warming hole”: Aerosol indirect effect 

27 and precipitable water vapor. Scientific Reports, 4 , 6929. http://dx.doi.org/10.1038/srep06929 

28 Zhang, X., L. Alexander, G.C. Hegerl, P. Jones, A.K. Tank, T.C. Peterson, B. Trewin, and F.W. 

29 Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and 

30 precipitation data. Wiley Interdisciplinary Reviews: Climate Change, 2 , 851-870. 

3 1 http ://dx .doi .org / 10.1 002/wcc . 1 47 

32 Zou, C.-Z. and J. Li, 2014: NOAA MSU Mean Layer Temperature. 35 pp. 

33 N O A A/NES DIS/S T AR . 

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35 .pdf 


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1 Zwiers, F.W., X.B. Zhang, and Y. Feng, 2011: Anthropogenic Influence on Long Return Period 

2 Daily Temperature Extremes at Regional Scales. Journal of Climate, 24 , 881-892. 

3 http ://dx .doi .org/ 10. 1175/201 Oj cli3 90 8 . 1 


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i 7. Precipitation Change in the United States 


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KEY FINDINGS 

1. There are sizeable regional and seasonal differences in precipitation changes since 1901. 
Annual precipitation has decreased in much of the West, Southwest and Southeast, and 
increased in most of the Northern and Southern Plains, Midwest and Northeast. A 
national average increase of 4% in annual precipitation since 1901 is mostly a result of 
large increases in the fall season. (. Medium confidence) 

2. Heavy precipitation events across the United States have increased in both intensity and 
frequency since 1901. There are important regional differences in trends, with the largest 
increases occurring in the northeastern United States. ( High confidence) 

3. The frequency and intensity of heavy precipitation events are projected to continue to 
increase over the 21st century (high confidence). However, there are regional and 
seasonal differences in projected changes in total precipitation with the northern United 
States, including Alaska getting wetter in the winter and spring, and parts of the 
southwest United States getting drier in the winter and spring ( medium confidence). 

4. Northern Hemisphere spring snow cover extent, North America maximum snow depth, 
and extreme snowfall years in the southern and western United States, have all declined 
while extreme snowfall years in parts of the northern United States, have increased 

(i medium confidence). Projections indicate large declines in snowpack in the western 
United States and shifts to more precipitation falling as rain than snow in the cold season 
in many parts of the central and eastern United States (high confidence). 

Introduction 

Changes in precipitation are one of the most important potential outcomes of a warming world, 
because precipitation is integral to the very nature of society and ecosystems. These systems 
have developed and adapted to the past envelope of precipitation variations. Any large changes 
beyond the historical envelope may have profound societal and ecological impacts. 

Historical variations in precipitation, as observed from both instrumental and proxy records, 
establish the context around which future projected changes can be interpreted because it is 
within that context that systems have evolved. Long-tenn station observations from core climate 
networks serve as a primary source to establish observed changes in both means and extremes. 
Proxy records, which are used to reconstruct past climate conditions, are varied and include 
sources such as tree ring and ice core data. Projected changes are examined using the Coupled 
Model Intercomparison Project Phase 5 (CMIP5) suite of model simulations. They establish the 
likelihood of distinct regional and seasonal patterns of change. 


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7.1 Historical Changes 

7.1.1 Mean Changes 

Annual precipitation averaged across the United States has increased approximately 4% over the 
1901-2015 period, slightly less than the 5% increase reported in the Third National Climate 
Assessment (NCA3) over the 1901-2012 period (Walsh et al. 2014). This slight decrease appears 
to be the result of the recent lingering droughts in the western and southwestern United States 
(NOAA 2016a; Barnston and Lyon 2016). The current meteorological drought in California 
began in late 2011 (Seager et al. 2015; NOAA 2016b). Further, there continue to be important 
regional and seasonal differences in precipitation changes (Figure 7.1). Seasonally, national 
increases are largest in the fall, while little change is observed for winter. Regional differences 
are apparent, as the Northeast, Midwest, and Great Plains have had increases while parts of the 
Southwest and Southeast have had decreases. The year 2015 was the third wettest on record, just 
behind 1973 and 1983 (all of which were years marked by El Nino events). Interannual 
variability is substantial, as evidenced by large multiyear meteorological and agricultural 
droughts in the 1930s and 1950s. 

[INSERT FIGURE 7.1 HERE: 

Figure 7.1: Annual and seasonal changes in precipitation over the contiguous United States. 
Changes are the average for present-day (1986-2015) minus the average for the first half of the 
last century (1901-1960 for the contiguous United States, 1925-1960 for Alaska and Hawaii) 
divided by the average for the first half of the century. (Figure source: Panel 1 : adapted from 
Peterson et al. 2013, © American Meteorological Society. Used with permission; Panels 2-5: 
NOAA NCEI, data source: nCLIMDiv)]. 

Trends differ markedly across the seasons, as do regional patterns of increases and decreases. 

Fall exhibits the largest (10%) and most widespread increase, exceeding 15% in much of the 
Northern Great Plains, Southeast, and Northeast. Winter has the smallest increase (2%), with 
drying over most of the western United States as well as parts of the Southeast. Spring and 
summer have comparable increases (about 3.5%) but substantially different patterns. In spring, 
the northern half of the contiguous United States has become wetter and the southern half has 
become drier. In summer, there is a mixture of increases and decreases across the Nation. Alaska 
shows little change in annual precipitation (+1.5%), however in all seasons central Alaska shows 
declines, and the panhandle shows increases. Hawaifi shows a decline of more than 15% in 
annual precipitation. 

7.1.2 Snow 

Changes in snow cover extent (SCE) in the Northern Hemisphere exhibit a strong seasonal 
dependence. There has been little change since the 1960s (when the first satellite records became 
available) in the winter, while fall SCE has increased. However, spring SCE has declined, due in 


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Chapter 7 


part to higher temperatures that shorten the time snow spends on the ground in the spring. This 
tendency is highlighted by the recent occurrences of both unusually high and unusually low 
monthly (October-June) SCE values, including top 5 highest and top 5 lowest values in the 48 
years of data. From 2010 onward, 7 of the 45 highest monthly SCE values occurred, all in the 
fall or winter (mostly in November and December), while 9 of the 10 lowest May and June 
values occurred. This reflects the trend toward earlier spring snowmelt, particularly at high 
latitudes, while little trend is noted in extreme fall SCE (Kunkel et al. 2016). The seasonal 
maximum snow depth has decreased and shifted to an earlier date over North America since 
1951 (Kunkel et al. 2016). There has been a decrease in the frequency of large snowfall years 
(years exceeding the 90th percentile) in the southern United States and the U.S. Pacific 
Northwest and an increase in the frequency of large snowfall years in the northern United States 
(Kluver and Leathers 2015). In the snow belts of the Great Lakes, lake effect snowfall has 
increased overall since the early 20th Century for Lakes Superior, Michigan-Huron, and Erie 
(Kunkel et al. 2010). However, individual studies for Lakes Michigan (Bard and Kristovich 
2012) and Ontario (Harnett et al. 2014) indicate that this increase has not been continuous. In 
both cases, upward trends were observed till the 1970s/early 1980s. However, since then lake 
effect snowfall has decreased in these regions. 

7.1.3 Observed changes in U.S. seasonal extreme precipitation. 

Extreme precipitation events occur when the air is nearly completely saturated. Hence, extreme 
precipitation events are generally observed to increase in intensity by about 6% to 7% for each 
degree Celsius of temperature increase, as dictated by the Clausius-Clapeyron relation. Figure 
7.2 shows the observed change in the 20-year return value of the seasonal maximum 5-day 
precipitation totals (rx5day) over the period 1948 to 2015. A mix of increases and decreases in 
individual weather stations is observed. However, well over two-thirds of the stations exhibit 
statistically significant increases, consistent with theoretical expectations and the observed 
changes in atmospheric moisture content. 

[INSERT FIGURE 7.2 HERE: 

Figure 7.2: Observed changes in the 20-year return value of the seasonal daily precipitation totals 
over the period 1948 to 2015 using data from the Global Historical Climatology Network 
(GHCN) dataset. (Figure source: adapted from Kunkel et al. 2013; © American Meteorological 
Society. Used with permission.)] 

Another metric of extreme precipitation, the annual maximum daily precipitation total, was 
calculated for the period 1901-2015. Those events exceeding a 5-year return value (essentially 
the top 20% of all annual maximum values) were averaged for 1986-2015 and 1901-1960. The 
difference between these two periods (Figure 7.3) indicates substantial increases over the eastern 
United States, particularly the northeast United States. The increases are much smaller over the 
western United States, with the southwest and northwest United States showing little increase. 


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[INSERT FIGURE 7.3 HERE: 


Figure 7.3: Percentage difference between 1901-1960 average and 1981-2015 average of top 

20% (events exceeding the threshold for a 5-year return period) of annual maximum daily 



precipitation values in each period using 930 U.S. stations from the Global Historical 
Climatology Network (GHCN). The percentages are first calculated for individual stations, then 


averaged over 2° latitude by 2° longitude grid boxes, and finally averaged over each NCA4 
region. (Figure source: CICS-NC / NOAA NCEI)] 

Figure 7.4 shows an update of a U.S. index of extreme precipitation from NCA3. This is the 
number of 2-day precipitation events exceeding the threshold for a 5-year recurrence, calculated 
over the period of 1896-2015. The number of events has been well above average for the last 
three decades. The slight drop from 2006-2010 to 2011-2015 reflects a below average number 
during the widespread severe meteorological drought year of 2012, while the other years in this 
pentad were well above average. The index value for 2015 was 80% above the 1901-1960 
reference period average and the third highest value in the 120 years of record (after 1998 and 
2008). 


[INSERT FIGURE 7.4 HERE: 


Figure 7.4: Index of the number of 2-day precipitation events exceeding the station-specific 


threshold for a 5-year recurrence interval. The annual values are averaged over 5-year periods 


with the pentad label indicating the ending year of the period. Annual time series of the number 

of events are first calculated at individual stations. Next, the grid box time series are calculated 

as the average of all stations in the grid box. Finally, a national time series is calculated as the 


average of the grid box time series. Data source: GHCN-Daily. (Figure source: CICS-NC / 



NOAA NCEI)] 

7.1.4 Extratropical Cyclones and Precipitation 

A large percentage of the extreme precipitation events in the United States are caused by 
extratropical cyclones (ETCs) and their associated fronts (Kunkel et al. 2012). In the northern 
United States, this is the case even in the summer when a sizeable fraction of extreme events 
occurs. The number of strong ETCs over North America in the summer has decreased since 1979 
by more than 35% (Chang et al. 2016), and overall ETC activity has decreased over this same 
time period. Most climate models simulate little change over this same historical period, but they 
project a decrease in summer ETC activity during the remainder of the 21st century (Chang et al. 
2016). This suggests that in the future there may be fewer opportunities in the summer for 
extreme precipitation, although increases in water vapor are likely to overcompensate for any 
decreases in ETCs by increasing the likelihood that an ETC will produce excessive rainfall 
amounts. An idealized set of climate simulations (Pfahl et al. 2015) suggests that substantial 
projected warming will lead to a decrease in the number of ETCs but an increase in the intensity 
of the strongest ETCs. Thus, the most extreme precipitation events associated with ETCs may be 
even greater in the future. 


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1 7.1.5 Detection and Attribution 

2 TRENDS 

3 Detectability of trends (compared to internal variability) for a number of precipitation metrics 

4 over the continental United States has been examined, however, trends identified for the United 

5 States regions have not been clearly attributed to anthropogenic forcing (Anderson et al. 2015; 

6 Easterling et al. 2016). One study concluded that increasing precipitation trends in some north- 

7 central U.S. regions and the extreme annual anomalies there in 2013 were at least partly 

8 attributable to the combination of anthropogenic and natural forcing (Knutson et al. 2014). 

9 At the global scale there is medium confidence that anthropogenic forcing has contributed to 

10 global-scale intensification of heavy precipitation over land regions with sufficient data coverage 

1 1 (Bindoff et al. 2013). Global changes in extreme precipitation have been attributed to 

12 anthropogenically forced climate change (Min et al. 2011, 2013), including annual maximum 1- 

13 day and 5-day accumulated precipitation over northern hemisphere land regions and (relevant to 

14 this report) over the North American continent (Zhang et al. 2013). Although the United States 

15 was not separately assessed, the parts of North America with sufficient data for their analysis 

16 included the continental United States, and parts of southern Canada, Mexico and Central 

17 America. Since the covered region was, predominantly over the United States, their 

18 detection/attribution findings are applicable to the continental United States. 

19 Analyses of precipitation extreme changes over the U.S. by region (20-year return values of 

20 seasonal daily precipitation over 1948-2015, Figure 7.2) show statistically significant increases 

21 consistent with theoretical expectations and previous analyses (Westra et al. 2013). Further, a 

22 significant increase in the area affected by precipitation extremes over North America has also 

23 been detected (Dittus et al. 2015). Extreme rainfall from U.S. landfalling tropical cyclones has 

24 been higher in recent years (1994-2008) than the long-term historical average, even accounting 

25 for temporal changes in stonn frequency (Kunkel et al. 2010). 

26 Based on current evidence it is concluded that detectable but not attributable increases in mean 

27 precipitation have occurred over parts of the central United States. Formal detection-attribution 

28 studies indicate a human contribution to extreme precipitation increases over the continental 

29 United States, but confidence is low based on those studies alone due to the short observational 

30 period, high natural variability, and model uncertainty. 

31 In summary, based on available studies, it is concluded that for the continental United States 

32 there is high confidence in the detection of extreme precipitation increases, while there is low 

33 confidence in attributing the extreme precipitation changes purely to anthropogenic forcing. 

34 There is stronger evidence for a human contribution ( medium confidence) when taking into 

35 account process-based understanding (increased water vapor in a warmer atmosphere), evidence 

36 from weather and climate models, and trends in other parts of the world. 


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EVENT ATTRIBUTION 

A number of recent heavy precipitation events have been examined to detennine the degree to 
which their occurrence and severity can be attributed to human-induced climate change. Table 
7.1 summarizes available attribution statements for recent extreme U.S. precipitation events. 
Seasonal and annual precipitation extremes occurring in the north-central and eastern U.S. 
regions in 2013 were examined for evidence of an anthropogenic influence on their occurrence 
(Knutson et al. 2014). Increasing trends in annual precipitation were detected in the northern tier 
of states, March-May precipitation in the upper Midwest, and June-August precipitation in the 
eastern United States since 1900. These trends are attributed to some kind of external forcing 
(anthropogenic + natural) but could not be directly attributed to anthropogenic forcing alone. 
However, based on this analysis it is concluded that the probability of these kinds of extremes 
has been made more likely by anthropogenic forcing. 

The human influence on individual storms has been investigated with conflicting results. In 
particular, Hoerling et al. (2014) find that despite the expected human-induced increase in 
available moisture, the GEOS-5 model produces fewer extreme storms in Colorado during the 
fall season and attribute that behavior to changes in the large-scale circulation. However, Pall et 
al. (2016) find that such coarse models cannot produce the observed magnitude of precipitation 
due to resolution constraints. Based on a highly conditional set of hindcast simulations imposing 
the large-scale meteorology and a substantial increase in both the probability and magnitude of 
the observed precipitation accumulation magnitudes in that particular meteorological situation, 
their study could not address the question of whether such situations have become more or less 
probable. Extreme precipitation event attribution is inherently limited by the rarity of the 
necessary meteorological conditions and the limited number of model simulations that can be 
performed to examine rare events. This remains an open and active area of research. However, 
based on these two studies, the anthropogenic contribution to the 2013 Colorado heavy rainfall- 
flood event is unclear. 


[INSERT TABLE 7.1 HERE: 


Table 7.1: A list of U.S. extreme precipitation events for which attribution statements have been 

made. In the last column, “+” indicates that an attributable human-induced increase in frequency 

and/or magnitude was found, 

“ indicates that an attributable human-induced decrease in 



frequency and/or magnitude was found, “0” indicates no attributable human contribution was 



identified. As in tables 6.1 and 8.2, several of the events were originally examined in the Bulle 


of the American Meteorological Society’s (BAMS) State of the Climate Reports and reexami 

by Angelil et al. (2016). In these cases, both attribution statements are listed with the original 

authors first. Source: M. Wehner.] 


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1 7.2 Projections 

2 Changes in precipitation in a warmer climate are governed by many factors. Although energy 

3 constraints can be used to understand global changes in precipitation, projecting regional 

4 changes is much more difficult because of uncertainty in projecting changes in the large-scale 

5 circulation that plays important roles in the formation of clouds and precipitation (Shepherd 

6 2014). For the contiguous United States (CONUS), future changes in seasonal average 

7 precipitation will include a mix of increases, decreases, or little change, depending on location 

8 and season (Figure 7.6). High-latitude regions are generally projected to become wetter while the 

9 subtropical zone is projected to become drier. As the CONUS lies between these two regions, 

10 there is significant uncertainty about the sign and magnitude of future anthropogenic changes to 

1 1 seasonal precipitation in much of the region, particularly in the middle latitudes of the nation. 

12 However, because the physical mechanisms controlling extreme precipitation differ from those 

13 controlling seasonal average precipitation (Section 7. 1 .4), in particular atmospheric water vapor 

14 will increase with increasing temperatures, confidence is high that projected future precipitation 

15 extremes will increase in frequency and intensity throughout the CONUS. 

16 Global climate models used to project precipitation changes exhibit varying degrees of fidelity in 

17 capturing the observed climatology and seasonal variations of precipitation across the United 

18 States. Global or regional climate models with higher horizontal resolution generally achieve 

19 better skill than the CMIP5 models in capturing the spatial patterns and magnitude of winter 

20 precipitation in the western and southeastern United States (e.g., Mearns et al. 2012; Wehner 

21 2013; Bacmeister et al. 2014; Wehner et al. 2014), leading to improved simulations of snowpack 

22 and runoff (e.g., Rauscher et al. 2008; Rasmussen et al. 2011). Simulation of present and future 

23 summer precipitation remains a significant challenge, as current convective parameterizations 

24 fail to properly represent the statistics of mesoscale convective systems (Klein et al. 2012). As a 

25 result, high-resolution models that still require the parameterization of deep convection exhibit 

26 mixed results (Wehner et al. 2014; Sakaguchi et al. 2015). Advances in computing technology 

27 are beginning to enable regional climate modeling at the higher resolutions (1-4 km), permitting 

28 the direct simulation of convective clouds systems (e.g., Ban et al. 2014) and eliminating the 

29 need for this class of parameterization. However, projections from such models are not yet ready 

30 for inclusion in this report. 

3 1 Important progress has been made by the climate modeling community in providing multimodel 

32 ensembles such as CMIP5 (Taylor et al. 2012) and NARCCAP (Mearns et al. 2012) to 

33 characterize projection uncertainty arising from model differences, and large ensemble 

34 simulations such as CESM-LE (Kay et al. 2015) to characterize uncertainty inherent in the 

35 climate system due to internal variability. 

36 Projections in this report from the CMIP5 climate model database is based both on model 

37 independence and a multivariate measure of skill over North America as described in section 

38 4.4.2. The model skill metrics in simulating seasonal average and extreme precipitation are 


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shown in figure 7.5. The extreme precipitation index is the seasonal maximum pentad total, 
defined as rx5day in the Expert Team on Climate Change Detection Indices (see climdex.org). 


[INSERT FIGURE 7.5 HERE: 


Figure 7.5: Relative performance of the CMIP5 models used in this study in simulating 



observed North American precipitation indices. Performance in simulating seasonal maxima 

| 

pentad precipitation is shown in the top four rows. The next four rows show perfonnance in 

replicating seasonal average precipitation (winter, spring, summer, fall). The bottom row is 

combination of all eight precipitation performance metrics. Models are ordered from left (best) to 
right (worst) as determined by this combined metric. (Figure source: adapted from Sanderson et 


ah, 2016] 


7.2.1 Future Changes in U.S. Seasonal Mean Precipitation. 

In the United States, projected changes in seasonal mean precipitation span the range from 
profound decreases to profound increases. And in many regions and seasons, projected changes 
in precipitation are not large compared to natural variations. The general pattern of change is 
clear and consistent with theoretical expectations. Figure 7.6 shows the weighted CMIP5 multi- 
model average seasonal change at the end of the century compared to the present under the 
RCP8.5 scenario (see Ch. 4: Projections for discussion of RCPs). In this figure, changes 
projected with high confidence to be larger than natural variations are stippled. Regions where 
future changes are projected with high confidence to be smaller than natural variations are 
hashed. In winter and spring, the northern part of the country is projected to become wetter as the 
global climate warms. In the early to middle parts of this century, this will likely be manifested 
as increases in snowfall (O’Gorman 2014). Later on, as temperature continues to increase, it will 
be too warm to snow in many current snow-producing situations, and precipitation will mostly 
be rainfall. In the southwestern United States, precipitation will decrease in the spring but the 
changes are only a little larger than natural variations. Many other regions of the country will not 
experience significant changes in average precipitation. This is also the case over most of the 
country in the summer and fall. 

[INSERT FIGURE 7.6 HERE: 

Figure 7.6: CMIP5 weighted multi-model seasonal average precipitation percent change in the 
2070-2100 period relative to the 1976-2005 average under the RCP8.5 pathway. Stippling 
indicates that changes are assessed to be large compared to natural variations. Hashing indicates 

that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

where projections are assessed to be inconclusive. Data source: World Climate Research 
Program’s (WCRP's) Coupled Model Intercomparison Project. (Figure source: NOAA NCEI)]. 


This pattern of projected precipitation change arises because of changes in locally available 
water vapor and weather system shifts. In the northern part of the continent, increases in water 
vapor, together with changes in circulation that are the result of expansion of the Hadley cell, 


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bring more moisture to these latitudes while maintaining or increasing the frequency of 
precipitation-producing weather systems. This change in the Hadley circulation also causes the 
subtropics to be drier in warmer climates as well as moving the mean storm track northward and 
away from the subtropics, decreasing the frequency of precipitation-producing systems. The 
combination of these two factors results in precipitation decreases in the southwestern United 
States, Mexico, and the Caribbean (Collins et al. 2013). 

PROJECTED CHANGES IN SNOW 

The Third National Climate Assessment (Georgakakos et al. 2014) projected reductions in 
annual snowpack of up to 40% in the western United States based on the SRES A2 emissions 
scenario in the CMIP3 suite of climate model projections. Recent research using the CMIP5 suite 
of climate model projections forced with the RCP8.5 scenario and statistically downscaled for 
the western United States continues to show the expected declines in various snow metrics, 
including snow water equivalent, the number of extreme snowfall events, and number of 
snowfall days (Lute et al. 2015). A northward shift in the rain-snow transition zone in the central 
and eastern United States was found using statistically downscaled CMIP5 simulations forced 
with RCP8.5. By the end of the 21st century, large areas that are currently snow-dominated in 
the cold season are expected to be rainfall dominated (Ning and Bradley 2015). 

7.2.2 Extremes 

HEAVY PRECIPITATION EVENTS 

Studies project that the observed increase in heavy precipitation events will continue in the future 
(e.g. Janssen et al. 2014, 2016). Similar to observed changes, increases are expected in all 
regions, even those regions where total precipitation is projected to decline, such as the 
southwestern United States. Under the RCP8.5 scenario the number of extreme events 
(exceeding a 5 -year return period) increases by 2 to 3 times the historical average in every region 
(Figure 7.7) by the end of the 21st century, with the largest increases in the Northeast. Under the 
RCP4.5 scenario, increases are 50%-100%. Research shows that there is strong evidence, both 
from the observed record and modeling studies, that increased water vapor resulting from higher 
temperatures is the primary cause of the increases (Kunkel et al. 2013a, b; Wehner 2013). 
However, additional effects on extreme precipitation due to changes in dynamical processes are 
poorly understood. 

[INSERT FIGURE 7.7 HERE: 

Figure 7.7. Regional extreme precipitation event frequency for RCP4.5 (green) and RCP8.5 
(blue) for a 2-day duration and 5-year return. Calculated for 2006-2100 but decadal anomalies 

begin in 201 1 . Error bars are ±1 standard deviation. (Figure source: Janssen et al. 2014)] 

Projections of changes in the 20-year return period amount for daily precipitation (Figure 7.8) 
using LOCA downscaled data also show large percentage increases for both the middle and late 


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21 st century. The lower emissions projections (RCP4.5) show increases of around 10% for mid- 
century, and up to 14% for the late century projections. The higher emissions projections show 
even large increases for both mid-century and the late century projections. No region in either 
emissions scenario shows a decline in heavy precipitation. 


[INSERT FIGURE 7.8 HERE: 


Figure 7.8: Projected change in 

the 20-year return period amount for daily precipitation for mid- 

and late-21st century for RCP4.5 and RCP8.5 emissions scenarios using LOCA downscaled data. 


Figure source: CICS-NC / NO A A NCEI)] 

HURRICANE PRECIPITATION 


For precipitation from hurricanes, several studies have projected increases of precipitation rates 
over ocean regions (Knutson et al. 2010), including for the Atlantic basin in particular (Knutson 
et al. 2013). The primary physical mechanism for this increase is the enhanced water vapor 
content in the warmer atmosphere, which enhances moisture convergence into the storm for a 
given circulation strength, although a more intense circulation can also contribute (Wang et al. 
2015). In a set of idealized forcing experiments, this effect was partly offset by differences in 
warming rates at the surface and at altitude (Villarini et al. 2014). Regional model projections of 
precipitation from landfalling tropical cyclones over the United States, based on downscaling of 
CM3 and CMIP5 model climate changes, suggest that the 21st century CMIP5-based projected 
occurrence frequency of post-landfall tropical cyclones over the United States showed little 
change compared to present day, as the reduced frequency of tropical cyclones over the Atlantic 
domain was mostly offset by a greater landfalling fraction. CM3-based projections showed a 
reduced occurrence frequency over U.S. land. The average tropical cyclone rainfall rates within 
500 km (about 311 miles) of the storm center increased by 8% to 17% in the simulations, which 
was at least as much as expected from the water vapor content increase factor alone. 


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1 TRACEABLE ACCOUNTS 

2 Key Message 1 

3 There are sizeable regional and seasonal differences in precipitation changes since 1901. Annual 

4 precipitation has decreased in much of the West, Southwest and Southeast, and increased in most 

5 of the Northern and Southern Plains, Midwest and Northeast. A national average increase of 4% 

6 in annual precipitation since 1901 is mostly a result of large increases in the fall season. (. Medium 

7 confidence) 

8 Description of evidence base 

9 The key message and supporting text summarizes extensive evidence documented in the climate 

10 science peer-reviewed literature. Evidence of long-term changes in precipitation is based on 

1 1 analysis of daily precipitation observations from the U.S. Cooperative Observer Network 

12 (http://www.nws.noaa.gov/om/coop/) and shown in Figure 7. 1 . Published work (refs) and Figure 

13 7.1 show important regional and seasonal differences in U.S. precipitation change since 1901. 

14 New Information and remaining uncertainties 

15 The main key issues that relates to uncertainty is the sensitivity of observed precipitation trends 

16 to the spatial distribution of observing stations, and to historical changes in station location, rain 

17 gauges, and observing practices. These issues are mitigated, somewhat, by new methods to 

18 produce spatial grids (Vose et al. 2014) through time. 

19 Assessment of confidence based on evidence 

20 Based on the evidence and understanding of the issues leading to uncertainties, confidence is 

21 high that average annual precipitation has increased in the U.S. Furthermore, confidence is also 

22 high, that the important regional and seasonal differences in changes documented in the text and 

23 in Figure 7.1 are robust. 

24 

25 Key Message 2 

26 Heavy precipitation events across the United States have increased in both intensity and 

27 frequency since 1901. There are important regional differences in trends, with the largest 

28 increases occurring in the northeastern United States. {High confidence) 

29 Description of evidence base 

30 The key message and supporting text summarizes extensive evidence documented in the climate 

3 1 science peer-reviewed literature. Evidence of long-term changes in precipitation is based on 

32 analysis of daily precipitation observations from the U.S. Cooperative Observer Network 

33 (http://www.nws.noaa.gov/om/coop/) and shown in Figures 7.2, 7.3 and 7.4. 

34 New Information and remaining uncertaintites 

35 The main key issues that relates to uncertainty is the sensitivity of observed precipitation trends 

36 to the spatial distribution of observing stations, and to historical changes in station location, rain 


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1 gauges, and observing practices. These issues are mitigated, somewhat, by methods used to 

2 produce spatial grids through gridbox averaging. 

3 Assessment of confidence based on evidence 

4 Based on the evidence and understanding of the issues leading to uncertainties, confidence is 

5 high that heavy precipitation events have increased in the U.S. Furthermore, confidence is also 

6 high, that the important regional and seasonal differences in changes documented in the text and 

7 in Figures 7.2, 7.3, and 7.4 are robust. 

8 

9 Key Message 3 

10 The frequency and intensity of heavy precipitation events are projected to continue to increase 

1 1 over the 21st century (high confidence). However, there are regional and seasonal differences in 

12 projected changes in total precipitation with the northern United States, including Alaska getting 

13 wetter in the winter and spring, and parts of the southwest United States getting drier in the 

14 winter and spring ( medium confidence). 

15 Description of evidence base 

16 Evidence of future change in precipitation is based on climate model projections and our 

17 understanding of the climate system’s response to increasing greenhouse gases and on regional 

18 mechanisms behind the projected changes. 

19 New information and remaining uncertainties 

20 A key issue is how well climate models simulate precipitation, which is one of the more 

2 1 challenging aspects of weather and climate simulation. In particular, comparisons of model 

22 projections for total precipitation (from both CMIP3 and CMIP5, see Sun et al. 2015) by NCA3 

23 region show a spread of responses in some regions (e.g. southwest) such that they are opposite 

24 from the ensemble average response. The continental United States is positioned in the transition 

25 zone between expected drying in the sub-tropics and wetting in the mid- and higher-latitudes. 

26 There are some differences in the location of this transition between CMIP3 and CMIP5 models 

27 and thus there remains uncertainty in the exact location of the transition zone. 

28 Assessment of confidence based on evidence 

29 Based on evidence from climate model simulations and our fundamental understanding of the 

30 relationship of water vapor to temperature, confidence is high that extreme precipitation will 

3 1 increase in all regions of the United States. However, based on the evidence and understanding 

32 of the issues leading to uncertainties, confidence is medium that that more total precipitation is 

33 projected for the northern U.S. and less for the Southwest. 

34 

35 


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1 Key Message 4 

2 Northern Hemisphere spring snow cover extent, North America maximum snow depth, and 

3 extreme snowfall years in the southern and western United States, have all declined while 

4 extreme snowfall years in parts of the northern United States, have increased ( medium 

5 confidence). Projections indicate large declines in snowpack in the western United States and 

6 shifts to more precipitation falling as rain than snow in the cold season in many parts of the 

7 central and eastern United States (high confidence). 

8 Description of evidence base 

9 Evidence of historical changes in snow cover extent and reduction in extreme snowfall years is 

10 consistent with our understanding of the climate system’s response to increasing greenhouse 

1 1 gases. 

12 Furthermore, climate model continue to consistently show future declines in snowpack in the 

13 western United States. Recent model projections for the eastern United States also confirm a 

14 future shift from snowfall to rainfall during the cold season in colder portions of the central and 

15 eastern United States. 

16 New Information and remaining uncertainties 

17 The main key issues that relates to uncertainty is the sensitivity of observed snow changes to the 

18 spatial distribution of observing stations, and to historical changes in station location, rain 

19 gauges, and observing practices, particularly for snow. Another key issue is the ability of climate 

20 models to simulate precipitation, particularly snow. Future changes in the frequency and 

2 1 intensity of meteorological systems causing heavy snow are less certain than temperature 

22 changes. 

23 Assessment of confidence based on evidence 

24 Given the evidence base and uncertainties confidence is medium, that snow cover extent has 

25 declined in the United States and medium that extreme snowfall years have declined in recent 

26 years. Confidence is high that western United States snowpack will decline in the future, and 

27 confidence is medium that a shift from snow domination to rain domination will occur in the 

28 parts of the central and eastern United States cited in the text. 

29 


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1 TABLE 

2 Table 7.1: A list of U.S. extreme precipitation events for which attribution statements have been 

3 made. In the last column, “+” indicates that an attributable human-induced increase in frequency 

4 and/or magnitude was found, indicates that an attributable human-induced decrease in 

5 frequency and/or magnitude was found, “0” indicates no attributable human contribution was 

6 identified. As in tables 6.2 and 8.2, several of the events were originally examined in the BAMS 

7 State of the Climate Reports and reexamined by Angelil et al. (2016) In these cases, both 

8 attribution statements are listed with the original authors first. Source: M. Wehner. 


Authors 

Event year and 
duration 

region 

type 

Attribution 

statement 

Knutson et al. 2014 / 
Angelil et al. 2016 

ANN 2013 

U.S. Northern 
Tier 

Wet 

+/0 

Knutson et al. 2014 / 
Angelil et al. 2016 

MAM 2013 

U.S. Upper 
Midwest 

Wet 

+/+ 

Knutson et al. 2014 / 
Angelil et al. 2016 

JJA2013 

Eastern U.S. 
Region 

Wet 

+/- 

Edwards et al. 2014 

October 4-5, 2013 

South Dakota 

blizzard 

0 

Hoerling et al. 2014 

September 10-14, 
2013 

Colorado 

Wet 

0 

Pall et al. 2016 

September 10-14, 
2013 

Colorado 

Wet 

+ 


9 

10 


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Chapter 7 


1 FIGURES 



Annual Precipitation 


Precipitation (%) 

■■<•15 
^■-15 to -10 
H -10 to -5 
I I -5 toO 

□ 0105 

□ 5 to 10 
M 10 to 15 
■1 > 15 



Winter Precipitation 


Spring Precipitation 



Summer Precipitation 


Fall Precipitation 




2 

3 Figure 7.1: Annual and seasonal changes in precipitation over the contiguous United States. 

4 Changes are the average for present-day (1986-2015) minus the average for the first half of the 

5 last century (1901-1960 for the contiguous United States, 1925-1960 for Alaska and Hawaii) 

6 divided by the average for the first half of the century. (Figure source: Panel 1: adapted from 

7 Peterson et al. 2013, © American Meteorological Society. Used with permission; Panels 2-5: 

8 NO A A NCEI, data source: nCLIMDiv) 


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Observed Change 

in Daily, 20-year Return Level Precipitation 


Winter Spring 



1 

2 

3 

4 

5 

6 


Change (inches) 


< 0.0 0 . 0 - 0.10 0 . 11 - 0.20 0 . 21 - 0.30 0 . 31 - 0.40 > 0.40 

Figure 7.2: Observed changes in the 20-year return value of the seasonal daily precipitation 
totals over the period 1948 to 2015 using data from the Global Historical Climatology Network 
(GHCN) dataset. (Figure source: adapted from Kunkel et al. 2013; © American Meteorological 
Society. Used with permission.) 



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Observed Change 

in 5-year Extreme Precipitation Events 



Change (%) 



0-4 5-9 10-14 15+ 


2 Figure 7.3: Percentage difference between 1901-1960 average and 1981-2015 average of top 

3 20% (events exceeding the threshold for a 5-year return period) of annual maximum daily 

4 precipitation values in each period using 930 U.S. stations from the Global Historical 

5 Climatology Network (GHCN). The percentages are first calculated for individual stations, then 

6 averaged over 2° latitude by 2° longitude grid boxes, and finally averaged over each NCA4 

7 region. (Figure source: CICS-NC / NOAA NCEI) 

8 


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Chapter 7 


1 

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U.S.: 5-yr return, 2-day duration 


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Figure 7.4: Index of the number of 2-day precipitation events exceeding the station-specific 
threshold for a 5-year recurrence interval. The annual values are averaged over 5-year periods, 
with the pentad label indicating the ending year of the period. Annual time series of the number 
of events are first calculated at individual stations. Next, the grid box time series are calculated 
as the average of all stations in the grid box. Finally, a national time series is calculated as the 
average of the grid box time series. Data source: GHCN-Daily. (Figure source: CICS-NC / 
NOAA NCEI) 


269 


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Chapter 7 


1 

2 

3 

4 

5 

6 

7 

8 


North America Precipitation Skill 


Rx5day DJF 
Rx5day MAM 
Rx5day JJA 
Rx5day SON 
Rx5day ALL 
Mean Precip DJF 
Mean Precip MAM 
Mean Precip JJA 
Mean Precip SON 
Mean Precip ALL 



DC, ft CD i i lu. 

I W LO | | I 

c\J I ^ c\j c\j ZD 

LU qc f, LU LU Z 

J ^ i I I CO 

CO ^ I 1 CO CO 

C/3 03 CC W 

O 0=0 0 


Figure 7.5: Relative performance of the CMIP5 models used in this study in simulating observed 
North American precipitation indices. Performance in simulating seasonal maxima pentad 
precipitation is shown in the top four rows. The next four rows show performance in replicating 
seasonal average precipitation (winter, spring, summer, fall). The bottom row is a combination of 
all eight precipitation performance metrics. Models are ordered from left (best) to right (worst) 
as determined by this combined metric. (Figure source: adapted from Sanderson et ah, 2016) 


270 


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Chapter 7 


Projected Change (%) in Seasonal Precipitation 


Winter Spring 



<-30 -30 -20 -10 10 20 30 >30 


2 Figure 7.6: CMIP5 weighted multi-model seasonal average precipitation percent change in the 

3 2070-2100 period relative to the 1976-2005 average under the RCP8.5 pathway. Stippling 

4 indicates that changes are assessed to be large compared to natural variations. Hashing indicates 

5 that changes are assessed to be small compared to natural variations. Blank regions (if any) are 

6 where projections are assessed to be inconclusive. Data source: World Climate Research 

7 Program's (WCRP's) Coupled Model Intercomparison Project. Figure source: NOAA NCEI. 

8 


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Chapter 7 


1 



1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 . 0 - 


North Great Plains 


South Great Plains 


mmiM Biilii 

n? & <§> a? 9? <$> 

///////// AW///// 


'v'Tr'v' , ir'v / Tk /, w' , w'v 

Decade 


Decade 


2 Figure 7.7: Regional extreme precipitation event frequency for RCP4.5 (green) and RCP8.5 

3 (blue) for a 2-day duration and 5-year return. Calculated for 2006-2100 but decadal anomalies 

4 begin in 201 1. Error bars are ±1 standard deviation. (Figure source: Janssen et al. 2014) 


5 


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Chapter 7 


Projected Change 

in Daily, 20-year Extreme Precipitation 


Lower Emissions 

Mid-century Late-century 




Change (%) 


1 0-4 5-9 10-14 15+ 

2 Figure 7.8: Projected change in the 20-year return period amount for daily precipitation for mid- 

3 and late-2 1st century for RCP4.5 and RCP8.5 emissions scenarios using LOCA downscaled data. 

4 Figure source: CICS-NC / NOAA NCEI) 



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Chapter 7 


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31 the conterminous United States. Journal of Hydrometeorology , 13, 1131-1141. 

32 http://dx.doi.org/10T 175/JHM-D- 11-0108.1 

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1 Kunkel, K.E., L. Ensor, M. Palecki, D. Easterling, D. Robinson, K.G. Hubbard, and K. 

2 Redmond, 2009: A new look at lake-effect snowfall trends in the Laurentian Great Lakes 

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6 Kunkel, K.E., T.R. Karl, H. Brooks, J. Kossin, J. Lawrimore, D. Arndt, L. Bosart, D. Changnon, 

7 S.L. Cutter, N. Doesken, K. Emanuel, P.Y. Groisman, R.W. Katz, T. Knutson, J. O’Brien, 

8 C J. Paciorek, T.C. Peterson, K. Redmond, D. Robinson, J. Trapp, R. Vose, S. Weaver, M. 

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14 1402-1408. http://dx.doi.org/10.1002/grl.50334 

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16 Trends and Extremes in Northern Hemisphere Snow Characteristics. Current Climate 

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22 Flory, W. Gutowski, E.S. Takle, R. Jones, R. Leung, W. Moufouma-Okia, L. McDaniel, 

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32 Ning, L. and R.S. Bradley, 2015: Snow occurrence changes over the central and eastern United 

33 States under future warming scenarios. Scientific Reports, 5, 17073. 

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1 NOAA, 2016: Climate at a glance, http://www.ncdc.noaa.gov/cag/time- 

2 series/us/ 1 07/0/pdsi/ 12/12/1895- 

3 201 6?base_prd=true&firstbaseyear= 1901 &lastbaseyear=2000 

4 NOAA, 2016: Climate at a glance, http://www.ncdc.noaa.gov/cag/time- 

5 series/us/4/0/pdsi/l 2/9/1 895-20 16?base_prd=trae&firstbaseyear= 

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7 Nature, 512, 416-418. http://dx.doi.org/10.1038/naturel3625 

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9 anthropogenic contributions to heavy Colorado rainfall in September 2013. Weather and 

10 Climate Extremes, in review. 

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12 Giovannettone, K. Guirguis, T.R. Karl, R.W. Katz, K. Kunkel, D. Lettenmaier, G J. McCabe, 

13 C J. Paciorek, K.R. Ryberg, S. Schubert, V.B.S. Silva, B.C. Stewart, A.V. Vecchia, G. 

14 Villarini, R.S. Vose, J. Walsh, M. Wehner, D. Wolock, K. Wolter, C.A. Woodhouse, and D. 

15 Wuebbles, 2013: Monitoring and understanding changes in heat waves, cold waves, floods 

16 and droughts in the United States: State of knowledge. Bulletin of the American 

17 Meteorological Society, 94, 821-834. http://dx.doi.Org/10.1175/BAMS-D-12-00066.l 

18 Pfahl, S., P.A. O’Gorman, and M.S. Singh, 2015: Extratropical Cyclones in Idealized 

19 Simulations of Changed Climates. Journal of Climate, 28, 9373-9392. 

20 http ://dx .doi .org / 10.1 1 75/JCLI-D- 14-008 1 6 . 1 

21 Rasmussen, R., C. Liu, K. Ikeda, D. Gochis, D. Yates, F. Chen, M. Tewari, M. Barlage, J. 

22 Dudhia, W. Yu, K. Miller, K. Arsenault, V. Grubisic, G. Thompson, and E. Gutmann, 2011: 

23 High-Resolution Coupled Climate Runoff Simulations of Seasonal Snowfall over Colorado: 

24 A Process Study of Current and Warmer Climate. Journal of Climate, 24, 3015-3048. 

25 http://dx.doi.Org/10.1175/2010JCLI3985.l 

26 Rauscher, S.A., J.S. Pal, N.S. Diffenbaugh, and M.M. Benedetti, 2008: Future changes in 

27 snowmelt-driven runoff timing over the western US. Geophysical Research Letters, 35, 

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30 Ringler, and P.H. Lauritzen, 2015: Exploring a Multiresolution Approach Using AMIP 

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1 Shepherd, T.G., 2014: Atmospheric circulation as a source of uncertainty in climate change 

2 projections. Nature Geoscience, 7, 703-708. http://dx.doi.org/10.1038/ngeo2253 

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6 Technical Report NESDIS 144, 111 pp. National Oceanic and Atmospheric Administration, 

7 National Environmental Satellite, Data, and Information Service. 

8 http://www.nesdis.noaa.gov/technical reports/NOAA NESDIS Technical Report 144.pdf 

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10 design. Bulletin of the American Meteorological Society, 93, 485-498. 

11 http://dx.doi.Org/10.1175/BAMS-D-ll-00094.l 

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13 and K.A. Reed, 2014: Sensitivity of Tropical Cyclone Rainfall to Idealized Global-Scale 

14 Forcings. Journal of Climate, 27, 4622-4641. http://dx.doi.org/10.1175/JCLI-D-13-00780T 

15 Vose, R.S., S. Applequist, M. Squires, I. Durre, M J. Menne, C.N.W. Jr., C. Fenimore, K. 

16 Gleason, and D. Arndt, 2014: Improved Historical Temperature and Precipitation Time 

17 Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53, 

18 1232-1251. http://dx.doi.org/10.1175/JAMC-D-13-0248T 

19 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

20 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

21 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

22 Climate Change Impacts in the United States: The Third National Climate Assessment. 

23 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

24 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

25 Wang and Kotamarthi, 2014: Missing from list. 

26 Wang, C.-C., B.-X. Lin, C.-T. Chen, and S.-H. Lo, 2015: Quantifying the Effects of Long-Term 

27 Climate Change on Tropical Cyclone Rainfall Using a Cloud-Resolving Model: Examples of 

28 Two Landfall Typhoons in Taiwan. Journal of Climate, 28, 66-85. 

29 http://dx.doi .org/ 10 . 1 1 75/JCLI-D- 1 4-00044 . 1 

30 Wehner, M.F., 2013: Very extreme seasonal precipitation in the NARCCAP ensemble: Model 

3 1 performance and projections. Climate Dynamics, 40, 59-80. 

32 http://dx.doi.org/10.1007/s00382-012-1393-l 

33 Wehner, M.F., K.A. Reed, F. Li, Prabhat, J. Bacmeister, C.-T. Chen, C. Paciorek, P.J. Gleckler, 

34 K.R. Sperber, W.D. Collins, A. Gettelman, and C. Jablonowski, 2014: The effect of 

35 horizontal resolution on simulation quality in the Community Atmospheric Model, CAM5.1. 


279 



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Chapter 7 


1 Journal of Advances in Modeling Earth Systems, 6, 980-997. 

2 http ://dx .doi .org / 10.1 002/20 1 3MS000276 

3 Westra, S., L.V. Alexander, and F.W. Zwiers, 2013: Global Increasing Trends in Annual 

4 Maximum Daily Precipitation. Journal of Climate, 26, 3904-39 1 8 . 

5 http ://dx .doi .org / 1 0 . 1 1 75/JCLI-D- 1 2-00502 . 1 

6 Zhang, X., H. Wan, F.W. Zwiers, G.C. Hegerl, and S.-K. Min, 2013: Attributing intensification 

7 of precipitation extremes to human influence. Geophysical Research Letters, 40, 5252-5257. 

8 http ://dx .doi .org/ 10.1 002/grl .51010 


280 



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Chapter 8 


1 8. Droughts, Floods, and Hydrology 

2 KEY FINDINGS 

3 1 . Recent droughts and associated heat waves have reached record intensity in some regions of 

4 the United States, but, by geographical scale and duration, the Dust Bowl era of the 1930s 

5 remains the benchmark drought and extreme heat event in the historical record. ( Very high 

6 confidence ) 

7 2. The human effect on recent major U.S. droughts is complicated. Little evidence is found for a 

8 human influence on observed precipitation deficits but much evidence is found for a human 

9 influence on surface soil moisture deficits due to increased evapotranspiration caused by 

10 higher temperatures. ( High confidence) 

11 3. Future decreases in surface soil moisture over most of the United States are likely as the 

12 climate warms. {High confidence) 

13 4. Reductions in western U.S. winter and spring snowpack are projected as the climate warms. 

14 Under higher emissions scenarios, and assuming no change to current water-resources 

15 management, chronic, long-duration hydrological drought is increasingly possible by the end 

16 of this century. {Very high confidence) 

17 5. Detectable increases in seasonal flood frequency have occurred in parts of the central United 

18 States. This is to be expected in the presence of the increase in extreme downpours known 

19 with high confidence to be linked to a warming atmosphere, but formal attribution 

20 approaches have not certified the connection of increased flooding to human influences. 

21 {Medium confidence) 

22 8.1. Drought 

23 The word “drought” brings to mind abnormally dry conditions. However, the meaning of “dry” 

24 can be ambiguous and lead to confusion in how drought is actually defined. Three different 

25 classes of droughts are defined by NOAA and describe a useful hierarchal set of water deficit 

26 characterization, each with different impacts. “Meteorological drought” describes conditions of 

27 precipitation deficit. “Agricultural drought” describes conditions of soil moisture deficit. 

28 “Hydrological drought” describes conditions of deficit in runoff (NOAA 2008). Clearly these 

29 three characterizations of drought are related but are also different descriptions of water scarcity 

30 with different target audiences. In particular, agricultural drought is of concern to producers of 

3 1 food while hydrological drought is of concern to water system managers. Soil moisture is a 

32 function of both precipitation and evapotranspiration. Because potential evapotranspiration 

33 increases with temperature, anthropogenic climate change generally results in drier soils and 

34 often less runoff in the long term. In fact, under the RCP8.5 scenario (see Ch. 4 for a description 

35 of the RCP scenarios) at the end of the 2 1st century, no region of the planet is projected to 


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1 experience significantly higher levels of annual average surface soil moisture due to the 

2 sensitivity of evapotranspiration to temperature, even though much higher precipitation is 

3 projected in some regions (Collins et al. 2013). Runoff, on the other hand, is projected to both 

4 increase and decrease, depending on location and season under the same conditions, illustrating 

5 the complex relationships between the various components of the hydrological system. Hence, it 

6 is vital to describe precisely the definition of drought in any public discussion to avoid confusion 

7 due to this complexity. 

8 8.1.1. Historical Context 

9 The United States has experienced all three types of droughts in the past, always driven in at 

10 least some part by natural variations in seasonal and/or annual precipitation amounts. As the 

1 1 climate changes, we can expect that human activities will alter the effect of these natural 

12 variations. The “Dust Bowl” drought of the 1930s is still the most significant meteorological and 

13 agricultural drought experienced in the United States in terms of its geographic and temporal 

14 extent. However, even though it happened prior to most of the current global warming, human 

15 activities exacerbated the dryness of the soil by the farming practices of the time (Bennet et al. 

16 1936). Tree ring archives reveal that such droughts (in the agricultural sense) have occurred 

17 periodically over the last 1,000 years (Cook et al. 2004). Long climate model simulations suggest 

1 8 that such droughts lasting several years to decades occur naturally in the southwestern United 

19 States (Coats et al. 2015). The IPCC AR5 (Bindoff et al. 2013) concluded “there is low 

20 confidence in detection and attribution of changes in (meteorological) drought over global land 

2 1 areas since the mid-20th century, owing to observational uncertainties and difficulties in 

22 distinguishing decadal-scale variability in drought from long-tenn trends.” As they noted, this 

23 was a weaker attribution statement than the IPCC AR4, which had concluded “that an increased 

24 risk of drought was more likely than not due to anthropogenic forcing during the second half of 

25 the 20th century.” The weaker statement in AR5 reflected additional studies with conflicting 

26 conclusions on global drought trends (e.g., Sheffield et al. 2012; Dai 2013). The western North 

27 America region was noted as a region where determining if observed recent droughts were 

28 unusual compared to natural variability was particularly difficult, due to evidence from 

29 paleoclimate proxies of cases of central U.S. droughts during the past 1,000 years that were 

30 longer and more intense than historical U.S. droughts (Masson-Delmotte et al. 2013). Future 

3 1 projections of the anthropogenic contribution to changes in drought risk and severity must be 

32 considered in the context of the significant role of natural variability. 

33 8.1.2. Recent Major U.S. Droughts 

34 

35 Meteorological and agricultural drought 

36 The United States has suffered a number of very significant droughts of all types since 2011. 

37 Each of these droughts was a result of different persistent, large-scale meteorological patterns of 

38 mostly natural origins, with varying degrees of attributable human influence. Table 8. 1 


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summarizes available attribution statements for recent extreme U.S. meteorological and 
agricultural droughts. Statements about meteorological data are decidedly mixed, revealing the 
complexities in interpreting the low tail of the distribution of precipitation. Statements about 
agricultural drought consistently maintain a human influence if only surface soil moisture 
measures are considered. The single agricultural drought attribution study at root depth comes to 
the opposite conclusion. In all cases, these attribution statements are made without detection (see 
Section 3.2). The absence of moisture during the 2011 Texas/Oklahoma drought and heat wave 
was found to be a naturally occurring event whose likelihood was enhanced by the La Nina state 
of the ocean, but the human interference in the climate system still doubled the chances of 
reaching such high temperatures (Hoerling et al. 2013). This study illustrates that the effect of 
human induced climate change is combined with natural variations and can compound or inhibit 
the realized severity of any given extreme weather event. 

[INSERT TABLE 8.1 HERE: 

Table 8.1: A list of U.S. droughts for which attribution statements have been made. In the last 
column, “+” indicates that an attributable human induced increase in frequency and/or magnitude 
was found, “ indicates that an attributable human induced decrease in frequency and/or 
magnitude was found, “0” indicates no attributable human contribution was identified. As in 
tables 6.2 and 7.1, several of the events were originally examined in the Bulletin of the American 
Meteorological Society’s (BAMS) State of the Climate Reports and reexamined by Angelil et al. 
(2016). In these cases, both attribution statements are listed with the original authors first. 

Source: M. Wehner.] 

The Great Plains/Midwest drought of 2012 was the most severe summer meteorological drought 
in the observational record for that region (Hoerling et al. 2014). An unfortunate string of three 
different patterns of large-scale meteorology from May through August 2012 precluded the 
normal frequency of summer thunderstorms but was not predicted by the NOAA seasonal 
forecasts (Hoerling et al. 2014). Little influence of the global sea surface temperature (SST) 
pattern on meteorological drought frequency has been found in model simulations (Hoerling et 
al. 2014). No evidence of a human contribution to the 2012 precipitation deficit in the Great 
Plains and Midwest is consistently found (Rupp et al. 2013; Hoerling et al. 2014; Angelil et al. 
2016). However, again an increase in the chances of the unusually high U.S. 2012 temperatures, 
partly associated with resultant dry summer soil moisture anomalies, was attributed to the human 
interference to the climate system (Diffenbaugh and Scherer 2013), indicating the strong 
feedback between soil moisture and surface air temperature variability from both natural and 
anthropogenic causes during periods of low precipitation. One study found that most, but not all, 
of the 2012 surface moisture deficit in the Great Plains was attributable to the precipitation 
deficit (Livneh and Hoerling 2016). That study also noted that Great Plains deep soil moisture 
was higher than normal in 2012 despite the surface drying due to wet conditions in prior years, 
indicating the long timescales relevant below the surface (Livneh and Hoerling 2016). 


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1 The current California drought, which began in 201 1, is unusual in different respects. In this 

2 case, the precipitation deficit from 2011 to 2014 was a result of the “ridiculously resilient ridge” 

3 of high pressure. This very stable high-pressure system steered storms towards the north, away 

4 from the highly engineered California water resource system (Swain et al. 2014; Seager et al. 

5 2014, 2015). The ridge itself was due to a slow-moving high sea surface temperature (SST) 

6 anomaly, referred to as “The Blob” — a result of an anomalous atmospheric circulation pattern 

7 (Bond et al. 2015). A principal attribution question regarding the precipitation deficit concerns 

8 the causes of this SST anomaly. Observational records are not long enough and the anomaly was 

9 unusual enough that similarly long-lived structures have not been often seen before. Hence, 

10 attribution statements, such as that about an anthropogenic increase in the frequency of 

1 1 geopotential height anomalies similar to 2012-2014 (e.g., Swain et al. 2014), are without 

12 associated detection (Ch. 3: Detection and Attribution). A secondary attribution question 

13 concerns the anthropogenic precipitation response in the presence of this SST anomaly. In 

14 attribution studies with a prescribed 2013 SST anomaly, a consistent human increase in the 

15 chances of very dry California conditions was found (Angelil et al. 2016). 

16 As in 2012, anthropogenic climate change did increase the risk of the high temperatures in 

17 California (Seager et al. 2015; Diffenbaugh et al. 2015), further exacerbating the soil moisture 

18 deficit and the associated stress on irrigation systems. An anthropogenic contribution to 

19 commonly used measures of agricultural drought, including the Palmer Drought Severity Index 

20 (PDSI), was found in California (Diffenbaugh et al. 2015; Williams et al. 2015) and is consistent 

21 with previous projections of changes in PDSI (Dai et al. 2013; Wehner et al. 2011; Walsh et al. 

22 2014) and with an attribution study (Brown et al. 2008). Due to its simplicity, the PDSI has been 

23 criticized as being overly sensitive to higher temperatures and thus may exaggerate the human 

24 contribution to soil dryness (Milly and Dunne 2016). In fact, this study also finds that 

25 formulations of potential evaporation used in more complicated hydrologic models are similarly 

26 biased, undermining confidence in the magnitude but not the sign of projected surface soil 

27 moisture changes in a warmer climate. Seager et al. (2013) analyzed climate model output 

28 directly finding that precipitation minus evaporation in the southwest United States is projected 

29 to experience significant decreases in surface water availability leading to surface runoff 

30 decreases in California, Nevada, the Colorado River headwaters and Texas even in the near term. 

3 1 However, the Milly and Dunne criticisms also apply to most of the CMIP5 land surface model 

32 evapotranspiration fonnulations. Analysis of soil moisture at deeper levels reveals less sensitivity 

33 to temperature increases than to precipitation variations, which have increased over the 20th 

34 century (Cheng et al. 2016). Nonetheless, the warming trend has led to declines in a number of 

35 indicators, including Sierra snow water equivalent, that are relevant to hydrological drought 

36 (Mao et al. 2015). Attribution of the California drought and heat wave remains an interesting and 

37 controversial research topic. 

38 In summary, there has not been a formal identification of a human influence on past changes in 

39 United States meteorological drought through the analysis of precipitation trends. Some, but not 


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1 all. United States meteorological drought event attribution studies, largely in the “without 

2 detection” class, exhibit a human influence. Attribution of a human influence on past changes in 

3 U.S. agricultural drought are limited both by availability of soil moisture observations and a lack 

4 of sub-surface modeling studies. While a human influence on surface soil moisture trends has 

5 been identified with medium confidence, its relevance to agriculture may be exaggerated. 

6 Runoff and hydrological drought 

7 Several studies focused on the Colorado River basin in the United States using more 

8 sophisticated runoff models driven by the CMIP3 models (Christensen and Lettenmaier 2007; 

9 McCabe and Wolock 2007; Barnett and Pierce 2009; Barnett et al. 2008; Hoerling et al. 2009) 

10 showed that annual runoff reductions in a wanner climate occur through a combination of 

1 1 evapotranspiration increases and precipitation decreases, with the overall reduction in river flow 

12 exacerbated by human water demands on the basin’s supply. 

13 8.1.2. Projections of Future Droughts and Runoff 

14 The future changes in seasonal precipitation shown in Chapter 7: Precipitation Change (Figure 

15 7.6) would indicate that the western United States may experience chronic future precipitation 

16 deficits, particularly in the spring. Such deficits are not confidently projected in other portions of 

17 the country. However, future higher temperatures will very likely lead to greater frequencies and 

1 8 magnitudes of agricultural droughts throughout the continental United States as the resulting 

19 increases in evapotranspiration outpace projected precipitation increases (Collins et al. 2013). 

20 Figure 8.1 shows the weighted multimodel projection of the percent change in near-surface soil 

2 1 moisture at the end of the 2 1st century under the RCP8.5 scenario, indicating widespread drying 

22 over the entire continental United States. Previous National Climate Assessments (Karl et al. 

23 2009; Walsh et al. 2014) have discussed the implication of these future drier conditions in the 

24 context of the Palmer Drought Severity Index (PDSI), finding that the future normal condition 

25 would be considered drought at the present time, and that the incidence of “extreme drought” 

26 (PDSI < -4) would be significantly increased. Confidence that future soils will generally be drier 

27 at the surface is high, as the mechanisms leading to increased evapotranspiration in a warmer 

28 climate are elementary scientific facts. However, the land surface component models in the 

29 CMIP5 climate models vary greatly in their sophistication, causing the projected magnitude of 

30 both the average soil moisture decrease and the increased risk for agricultural drought to be less 

3 1 certain. The weighted projected seasonal decreases in surface soil moisture are generally towards 

32 drier conditions, even in regions and seasons where precipitation is projected to experience large 

33 increases (Figure 7.6) due to increases in the evapotranspiration associated with higher 

34 temperature. Drying is assessed to be large relative to natural variations in much of the CONUS 

35 region in the summer. Significant spring and fall drying is also projected in the mountainous 

36 western states, with potential implications for forest and wildfire risk. Also, the combination of 

37 significant summer and fall drying in the midwestern states has potential agricultural 

38 implications. The largest percent changes are projected in the Southwestern United States and are 


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1 consistent in magnitude with an earlier study of the Colorado River Basin using more 

2 sophisticated macroscale hydrological models (Christensen and Lettenmaier 2007). 

3 Despite the important usage of PDSI as an early warning indicator of U.S. drought (e.g., NOAA 

4 2016), its suitability as a measure of future agricultural drought in much wanner climates is 

5 questionable due to its simplified representation of the water cycle, resulting in overly 

6 pessimistic projections (Hoerling et al. 2012). Similarly, a direct CMIP5 multimodel projection 

7 of soil moisture such as in Figure 8.1 must be limited to the surface (defined as the top 10 cm of 

8 the soil), as the land surface component sub-models vary greatly in their representation of the 

9 total depth of the soil. A more relevant projection to agricultural drought would be the soil 

10 moisture at the root depth of typical U.S. crops, which is not generally available from the CMIP5 

1 1 models. Few of the CMIP5 land models have detailed ecological representations of 

12 evapotranspiration processes, causing the simulation of the soil moisture budget to be less 

13 constrained than reality (Williams and Torn 2015). Nonetheless, Figure 8. 1 shows a projected 

14 drying of surface soil moisture across nearly all of the coterminous United States in all seasons 

15 even in regions and seasons where precipitation is projected to increase. 

16 Changes in average total seasonal runoff — including surface streamflow and groundwater — 

17 differ significantly between the mountainous western United States, Alaska and the rest of the 

18 Nation. Figure 8.2 shows the projected end of the 21st century CMIP5 multimodel weighted 

19 average percent changes in near-total runoff under the RCP8.5 scenario, revealing increased 

20 runoff in Alaska and Northern Canada during winter due to the change from snow to rain in the 

21 wanner climate. Projected winter increases are assessed as large (Appendix B) in a small region 

22 of the Rockies as well. For the rest of the contiguous United States, the weighted projection for 

23 average total runoff is generally to be decreased as a result of increased evapotranspiration. 

24 However, these decreases are assessed to be small compared to natural variations in all seasons 

25 (Appendix B). 

26 Reduced contiguous U.S. snowfall accumulations in much warmer future climates are virtually 

27 certain as frozen precipitation is replaced by rain regardless of the projected changes in total 

28 precipitation amounts discussed in Chapter 7 (Figure 7.6). Widespread reductions in mean 

29 snowfall across North America are projected by the CMIP5 models (O’Gonnan 2014). Together 

30 with earlier snowmelt at altitudes high enough for snow, disruptions in western U.S. water 

3 1 delivery systems are expected to lead to more frequent hydrological drought conditions (Barnett 

32 et al. 2008; Pierce et al. 2008; Barnett and Pierce 2009; Cayan et al. 2010). The elevation of 

33 mountains as represented in the CMIP5 models is too low, due to resolution constraints, to 

34 adequately represent the effects of future temperature on snowpacks. However, increased model 

35 resolution has been demonstrated to have important impacts on future projections of snowpack 

36 change in warmer climates and is enabled by recent advances in high perfonnance computing 

37 (Kapnick and Delworth 2013). Figure 8.3 and Table 8.2 show a projection of changes in western 

38 U.S. mountain winter (December, January, and February) hydrology obtained from a different 

39 high-resolution atmospheric model at the middle and end of the 21st century under the RCP8.5 


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scenario. These projections indicate dramatic reductions in all aspects of snow (Rhoades et al. in 
review) and are similar to a previous statistically downscaled projections (Cayan et al. 2013; 

Klos et al. 2014). Given the larger projected increases in temperature at high altitudes compared 
to adjacent lower altitudes (Pierce and Cayan 2012) and the resulting changes in both snowpack 
depth and melt timing in very warm future scenarios, and assuming no change to water-resource 
management, several important western U.S. snowpack reservoirs effectively disappear by 2100 
in this dynamical projection, resulting in chronic, long-lasting hydrological drought. This 
dramatic statement is also supported by a multi-model statistical downscaling of the CMIP5 
RCP8.5 ensemble that finds large areal reductions in snow dominated regions of the western 
United States by mid-century and complete elimination of snow-dominated regions in certain 
watersheds (Klos et al. 2014). 

[INSERT FIGURE 8.1 HERE: 

Figure 8.1. Projected end of the 21st century weighted CMIP5 multimodel average percent 
changes in near surface seasonal soil moisture (mrsos) under the RCP8.5 scenario. Stippling 
indicates that changes are assessed to be large compared to natural variations. Hashing indicates 
that changes are assessed to be small compared to natural variations. Blank regions (if any) are 
where projections are assessed to be inconclusive (Appendix B).] 

[INSERT FIGURE 8.2 HERE: 

Figure 8.2. Projected end of the 21st century weighted CMIP5 multimodel average percent 
changes in total seasonal runoff (mrro) under the RCP8.5 emissions scenario. Stippling indicates 
that changes are assessed to be large compared to natural variations. Hashing indicates that 
changes are assessed to be small compared to natural variations. Blank regions (if any) are where 
projections are assessed to be inconclusive (Appendix B).] 

[INSERT FIGURE 8.3 HERE: 

Figure 8.3. Projected changes in winter (DJF) Snow Water Equivalent at the middle and end of 
this century under the RCP8.5 scenario from a high-resolution version of the Community 
Atmospheric Model, CAM5 (Rhoades et al. 2016), Figure source: Lawrence Berkeley National 
Laboratory.] 

8.2. Floods 

Flooding damage in the United States can come from flash floods of smaller rivers and creeks, 
prolonged flooding along major rivers, and coastal flooding from storm surge and the confluence 
of coastal storms and inland riverine flooding from the same precipitation event (Ch. 12: Sea 
Level Rise). Flash flooding is associated with extreme precipitation somewhere along the river 
which may occur upstream of the regions at risk. Flooding of major rivers in the United States 
usually occurs in the late winter or spring and can result from an unusually heavy seasonal 
snowfall followed by a “rain on snow” event or from a rapid onset of higher temperatures that 
leads to rapid snow melting within the river basin. Changes in streamflow rates depend on many 


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1 factors, both human and natural, in addition to climate change. Deforestation, urbanization, and 

2 changes in agricultural practices can all play a role in past and future changes in flood statistics. 

3 Projection of future changes is thus a multivariate problem (Walsh et al. 2014). 

4 Trends in extreme high values of streamflow are mixed across the United States, as reported in 

5 the Third National Climate Assessment (Walsh et al. 2014). Recent analysis of annual maximum 

6 streamflow shows statistically significant trends only in the upper Mississippi River valley 

7 (increasing) and in the Northwest (decreasing) (McCabe and Wolock 2014). This is seemingly in 

8 contrast to the much more widespread increasing trends in extreme precipitation over much of 

9 the eastern and northern United States. As noted above, floods are poorly explained by 

10 precipitation characteristics alone; the relevant mechanisms are more complex, involving 

1 1 processes that are seasonally and geographically variable, including the seasonal cycles of soil 

12 moisture content and snowfall/snowmelt (Berghuijs et al. 2016). The northeast United States is 

13 an interesting example. Strong increasing trends in extreme precipitation have been observed and 

14 appear to be ubiquitous across this region (Walsh et al. 2014; Frei et al. 2015). Trends in 

15 maximum streamflow are less dramatic and less spatially coherent (McCabe and Wolock 2014; 

16 Frei et al. 2015), although one study found mostly increasing trends (Armstrong et al. 2014) in 

17 that region, somewhat at odds with other studies. This apparent disparity is caused by the 

18 seasonality of the two phenomena. Extreme precipitation events are larger in the warm season 

19 when soil moisture and seasonal streamflow levels are low and less favorable for flooding. By 

20 contrast, high streamflow events are larger in the cold season when soil moisture is high and 

21 snowmelt and frozen ground can enhance runoff (Frei et al. 2015). A future projection study 

22 based on coupling an ensemble of regional climate model output to a hydrology model (Najafi 

23 and Moradkhani 2015) finds that the magnitude of very extreme runoff (which can lead to 

24 flooding) is decreased in most of the summer months in Washington State, Oregon, Idaho and 

25 western Montana but is substantially increased in the other seasons. Projected increases in 

26 extreme runoff from the coast to the Cascades are particularly large in the fall and winter. 

27 Thus, apparent disparities between extreme precipitation and flood trends are partially explained 

28 by the complex seasonal and geographic processes that affect flooding that go beyond simple 

29 precipitation characteristics. This presents a challenge for attribution studies and it has been 

30 suggested that additional scientific rigor is needed in flood attribution studies (Merz et al. 2012). 

3 1 The IPCC WG1 AR5 (Bindoff et al. 2013) did not attribute changes in flooding to anthropogenic 

32 influence nor report detectable changes in flooding magnitude or frequency. Analysis of 200 

33 U.S. stream gauges indicates both areas of increasing and decreasing flooding magnitude (Hirsch 

34 and Ryberg 2012) but does not provide robust evidence that these trends are detectible or 

35 attributable to human influences. Significant increases in flood frequency have been detected in 

36 about one-third of stream gauge stations examined for the central United States, with a much 

37 stronger signal of change than is found for flood magnitude in these gauges (Mallakpour and 

38 Villarini 2015). Although both temperature and precipitation increases were influencing the 


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1 flooding changes, no attribution of these changes to anthropogenic forcing has been claimed 

2 (Mallakpour and Villarini 2015). 

3 The nature of the proxy archives complicates the reconstruction of past flood events in a gridded 

4 fashion as has been done with droughts. However, reconstructions of past river outflows do exist. 

5 For instance, it has been suggested that the mid-20th century river allocations for the Colorado 

6 River were made during one of the wettest periods of the past five centuries (Woodhouse et al. 

7 2006). For the eastern United States, the Mississippi River has undergone century-scale 

8 variability in flood frequency — perhaps linked to the moisture availability in the central United 

9 States and the temperature structure of the Atlantic Ocean (Munoz et al. 2015). 

10 No studies have clearly attributed long-tenn changes in observed flooding of major rivers in the 

1 1 United States to anthropogenic forcing. We conclude that there is medium confidence that 

12 detectable, though not attributable, increases in seasonal flood frequency have occurred in parts 

13 of the central United States. 

14 Studies of localized extreme flooding events are extremely limited, are confined to changes in 

15 the locally responsible precipitation event, and do not include detailed analyses of the events’ 

16 hydrology. Gochis et al. (2015) describes the massive floods of 2013 along the Colorado front 

17 range, estimating that the record rainfall exceeds 1,000-year return values in some regions. 

18 Hoerling et al. (2014) analyzed the 2013 northeastern Colorado heavy multiday precipitation 

19 event and resulting flood finding little evidence of an anthropogenic influence on its occurrence. 

20 However, Pall et al. (2016) challenge their methodology with a more constrained study and find 

2 1 that the thennodynamic response of precipitation in this event due to anthropogenic forcing was 

22 substantially increased. The Pall et al. (2016) approach does not rule out that the likelihood of the 

23 extremely rare large-scale meteorological pattern responsible for the flood may have changed. 

24 8.3 Wildfires 

25 Recent decades have seen increased forest fire activity in the western United States and Alaska. 

26 For the western United States, one study has estimated that human-caused climate change was 

27 responsible for nearly half of the total forest acreage burned by wildfires over 1984 to 2015 

28 (Abatzoglou and Williams 2016) while another study found an increased risk of fire in California 

29 due to human-caused climate change, based on a model assessment of the 2014 fire season 

30 (Yoon et al. 2015). For Alaska, one study found that human caused climate change had increased 

3 1 the risk of severe fire seasons like 2015 by 34%-60% (Partain et al., in review). In Abatzouglou 

32 and Williams, modeled increases in temperatures and vapor pressure deficits due to 

33 anthropogenic climate change caused increased fire potential by increasing the aridity of forest 

34 fuels during the fire season. None of the studies demonstrates that a long tenn increase in forest 

35 fire activity is highly unusual in comparison to natural variability, as they are generally inferring 

36 a human-caused climate change contribution to trends or probabilities based on model 

37 calculations. The degree of forestry management, which is greater in the western United States 


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1 than in Alaska, is a confounding factor that complicates attribution of changes to anthropogenic 

2 climate change. We conclude that there is medium confidence for a human-caused climate 

3 change contribution to increased forest fire activity in Alaska in recent decades, but low 

4 confidence for a detectable human climate change contribution in the western United States 

5 based on existing studies. 

6 


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1 TRACEABLE ACCOUNTS 

2 Key Message 1 

3 Recent droughts and associated heat waves have reached record intensity in some regions of the 

4 United States, but, by geographical scale and duration, the Dust Bowl era of the 1930s remains 

5 the benchmark drought and extreme heat event in the historical record. ( Very high confidence) 

6 Description of evidence base 

7 Recent droughts are well characterized and described in the literature. The dust bowl is not as 

8 well documented, but available observational records support the key finding. 

9 Major uncertainties 

10 Record breaking temperatures are well documented with low uncertainty (Meehl et al 2009). The 

1 1 magnitude of the Dust Bowl relative to present times varies with location. Uncertainty in the key 

12 finding is affected by the quality of pre-WW2 observations but is relatively low. 

13 Assessment of confidence based on evidence and agreement 

14 X Very High 

15 □ High 

16 □ Medium 

17 □ Low 

18 Precipitation is well observed in the United States leading to very high confidence. 

19 Summary sentence or paragraph that integrates the above information 

20 The key finding is a statement that recent U.S. droughts, while sometimes long and severe, are 

2 1 not unprecedented in the history of Earth’s hydrologic natural variation. 

22 

23 Key Message 2 

24 The human effect on recent major U.S. droughts is complicated. Little evidence is found for a 

25 human influence on observed precipitation deficits but much evidence is found for a human 

26 influence on surface soil moisture deficits due to increased evapotranspiration caused by higher 

27 temperatures. {High confidence) 

28 Description of evidence base 

29 Observational records of meteorological drought are not long enough to detect statistically 

30 significant trends. Additionally, paleoclimatic evidence suggests that major droughts have 

3 1 occurred throughout the distant past. Surface soil moisture is not well observed throughout the 

32 CONUS but numerous event attribution studies attributes enhanced reduction of surface soil 

33 moisture during dry periods due to anthropogenic wanning and enhanced evapotranspiration. 

34 


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1 Major uncertainties 

2 Uncertainties stem from the length of precipitation observations and the lack of surface moisture 

3 observations. 

4 Assessment of confidence based on evidence and agreement 

5 □ Very High 

6 X High 

7 □ Medium 

8 □ Low 

9 Summary sentence or paragraph that integrates the above information 

10 The precipitation deficit portion of the key finding is a conservative statement reflecting the 

1 1 conflicting and limited event attribution literature on meteorological drought. The soil moisture 

12 portion of the key finding is limited to the surface and not the more relevant root depth and is 

13 supported by the studies cited in Chapter 8. 

14 

1 5 Key Message 3 

16 Future decreases in surface soil moisture over most of the United States are likely as the climate 

17 wanns. (. High confidence) 

18 Description of evidence base 

19 First principles establish that evaporation is at least linearly dependent on temperatures and 

20 accounts for much of the surface moisture decrease as temperature increases. Plant transpiration 

2 1 for many non-desert species controls plant temperature and responds to increased temperature by 

22 opening stomata to release more water vapor. This water comes from the soil at root depth as the 

23 plant exhausts its stored water supply (very high confidence). 

24 Major uncertainties 

25 While both evaporation and transpiration changes are of the same sign as temperature increases, 

26 the relative importance of each as a function of depth is less well quantified. The amount of 

27 transpiration varies considerably among plant species and these are treated with widely varying 

28 of sophistication in the land surface components of contemporary climate models. Uncertainty in 

29 the sign of the anthropogenic change of root depth soil moisture is low in regions and seasons of 

30 projected precipitation decreases (Chapter 7). Uncertainty in the magnitude of the change in soil 

3 1 moisture at all depths and all regions and seasons is not low. 

32 Assessment of confidence based on evidence and agreement 

33 □ Very High 

34 x High 


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1 □ Medium 

2 □ Low 

3 CMIP5 and regional models support the surface soil moisture key finding. 

4 Summary sentence or paragraph that integrates the above information 

5 In the northern United States, surface soil moisture (top 10 cm) is very likely to decrease as 

6 evaporation outpaces increases in precipitation. In the southwest, the combination of temperature 

7 increases and precipitation decreases causes surface soil moisture decreases to be virtually 

8 certain. In this region, decreases in soil moisture at the root depth is very likely. 

9 

10 Key Message 4 

1 1 Reductions in western U.S. winter and spring snowpack are projected as the climate warms. 

12 Under higher emissions scenarios, and assuming no change to current water-resources 

13 management, chronic, long-duration hydrological drought is possible by the end of this century. 

14 {Very high confidence). 

15 Description of evidence base 

16 First principles tell us that as temperatures rise, minimum snow levels also must rise. Certain 

17 changes in western U.S. hydrology have already been reported in the papers following Barnett et 

18 al. (2008). The CMIP3/5 models project widespread warming with future increases in 

19 atmospheric GHG concentrations, although these are underestimated in the current generation of 

20 GCMs at the high altitudes of the western U.S. due to constraints on orographic representation at 

21 current GCM spatial resolutions. 

22 CMIP5 models were not designed or constructed for direct projection of locally relevant 

23 snowpack amounts. However, a high-resolution climate model, selected for its ability to simulate 

24 Western U.S. snowpack amounts and extent, projects devastating changes in the hydrology of 

25 this region assuming constant water-resource management practices (Rhoades et al 2016). This 

26 conclusion is also supported by a statistical downscaling result shown in figure 3.1 of Walsh et 

27 al. 2014 and Cayan et al. 2013 and by the more recent statistical downscaling study of Klos et al. 

28 2014. 

29 Major uncertainties 

30 The major uncertainty is not so much “if’ but rather “when” as changes to precipitation phase 

3 1 (rain or snow) are sensitive to temperature increases that in turn depends on GHG forcing 

32 changes. Also, changes to the lower elevation catchments will be realized prior to those at higher 

33 elevations that even at 25 km, is not adequately resolved. Uncertainty in the second statement 

34 also stems from the usage of one model. However, this simulation is a so-called “prescribed 

35 temperature” experiment with the usual uncertainties about climate sensitivity wired in by the 


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Chapter 8 


1 usage of one particular ocean temperature change. Uncertainty in the equator to pole differential 

2 ocean wanning rate is also a factor. 

3 Assessment of confidence based on evidence and agreement 

4 X Very High 

5 □ High 

6 □ Medium 

7 □ Low 

8 All CMIP5 models project large scale western U.S. warming as GHG forcing increases. 

9 Wanning is underestimated in most of the western United States due to elevation deficiencies 

10 that are a consequence of coarse model resolution. Snow melts above 32°F. 

1 1 Summary sentence or paragraph that integrates the above information 

12 Wanner temperatures lead to less snow and more rain if total precipitation remains unchanged. 

13 Projected winter/spring precipitation changes are a mix of increases in northern states and 

14 decreases in the southwest. In the northern Rockies, snowpack is projected decrease even with a 

15 projected precipitation increase due to this phase change effect. This will lead to, at the very 

16 least, profound changes to the seasonal and sub-seasonal timing of the western U.S. hydrological 

17 cycle even where annual precipitation remains nearly unchanged. 

18 

19 Key Message 5 

20 Detectable increases in seasonal flood frequency have occurred in parts of the central United 

21 States. This is to be expected in the presence of the increase in extreme downpours known with 

22 high confidence to be linked to a wanning atmosphere, but formal attribution approaches have 

23 not certified the connection of increased flooding to human influences. (. Medium confidence). 

24 Description of evidence base 

25 Observed increases are documented by Walsh et al. 2014 and other studies cited in the text. No 

26 attribution statements have been made. 

27 Major uncertainties 

28 Floods are highly variable both in space and time. The multi-variate nature of floods complicates 

29 detection and attribution. 

30 Assessment of confidence based on evidence and agreement 

31 □ Very High 

32 □ High 

33 X Medium 

34 □ Low 


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Chapter 8 


1 Summary sentence or paragraph that integrates the above information 

2 The key finding is a relatively weak statement reflecting the limited literature on the detection 

3 and attribution of anthropogenic changes in US flooding intensity, duration and frequency. 

4 

5 

6 TABLES 

7 Table 8.1: A list of U.S. droughts for which attribution statements have been made. In the last 

8 column, “+” indicates that an attributable human induced increase in frequency and/or magnitude 

9 was found, indicates that an attributable human induced decrease in frequency and/or 

10 magnitude was found, “0” indicates no attributable human contribution was identified. As in 

1 1 tables 6.2 and 7.1, several of the events were originally examined in the Bulletin of the American 

12 Meteorological Society’s (BAMS) State of the Climate Reports and reexamined by Angelil et al. 

13 (2016). In these cases, both attribution statements are listed with the original authors first. 

14 Source: M. Wehner. 

15 


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Chapter 8 


Authors 

Event Y ear and 
Duration 

Region or State 

Type 

Attribution 

Statement 

Rupp and Mote 2012 / 
Angelil et al. 2016 

MAMJJA 

2011 

Texas 

Meteorological 

+/+ 

Hoerling et al. 2013 

2012 

Texas 

Meteorological 

+ 

Rupp et al. 2013 / 
Angelil et al. 2016 

MAMJJA 

2012 

CO, NE, KS, OK, IA, 
MO, AR & IL 

Meteorological 

0/0 

Rupp et al. 2013 / 
Angelil et al. 2016 

MAM 2012 

CO, NE, KS, OK, IA, 
MO, AR & IL 

Meteorological 

0/0 

Rupp et al. 2013 / 
Angelil et al. 2016 

JJA 2012 

CO, NE, KS, OK, IA, 
MO, AR & IL 

Meteorological 

0/+ 

Hoerling et al. 2014 

MJJA 2012 

Great Plains/Midwest 

Meteorological 

0 

Swain et al. 2014 / 
Angelil et al. 2016 

ANN 2013 

California 

Meteorological 

+/+ 

Wang and Schubert 
2014 / Angelil et al. 
2016 

JS 2013 

California 

Meteorological 

0/+ 

Knutson et al. 2014 / 
Angelil et al. 2016 

ANN 2013 

California 

Meteorological 

+/+ 

Knutson et al. 2014 / 
Angelil et al. 2016 

MAM 2013 

U.S. Southern Plains 
region 

Meteorological 

+/+ 

Diffenbaugh et al. 2014 

2012-2014 

California 

Agricultural 

+ 

Seager et al. 2015 

2012-2014 

California 

Agricultural 

+ 

Cheng et al. 2016 

2011-2015 

California 

Agricultural 

- 


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Chapter 8 


1 Table 8.2: Projected changes in western U.S. mountain range winter (DJF) snow-related 

2 hydrology variables at the middle and end of this century. Projections are for the RCP8.5 

3 scenario from a high-resolution version of the Community Atmospheric Model, CAM5 (Rhoades 

4 et al. 2016). 


Mountain Range 

Snow 

Water 
Equivalent 
(% Change) 

Snow 

Cover 

(% Change) 

Snowfall 

(% Change) 

Surface 

Temperature 

(Change in K) 

2050 

2100 

2050 

2100 

2050 

2100 

2050 

2100 

Cascades 

- 41.5 

- 89.9 

- 21.6 

- 72.9 

- 10.7 

- 50.0 

0.9 

4.1 

Klamath 

- 50.7 

- 95.8 

- 38.6 

- 89.0 

- 23.1 

- 78.7 

0.8 

3.5 

Rockies 

- 17.3 

- 65.1 

- 8.2 

- 43.1 

1.7 

- 8.2 

1.4 

5.5 

Sierra Nevada 

- 21.8 

- 89.0 

- 21.9 

- 77.7 

- 4.7 

- 66.6 

1.1 

4.5 

Wasatch and Uinta 

- 18.9 

- 78.7 

- 14.2 

- 61.4 

4.1 

- 34.6 

1.8 

6.1 

Western USA 

- 22.3 

- 70.1 

- 12.7 

- 51.5 

- 1.6 

- 21.4 

1.3 

5.2 


6 


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Chapter 8 


1 

2 


3 

4 

5 

6 

7 

8 

9 


FIGURES 




Figure 8.1. Projected end of the 21st century weighted CMIP5 multimodel average percent 
changes in near surface seasonal soil moisture (mrsos) under the RCP8.5 scenario. Stippling 
indicates that changes are assessed to be large compared to natural variations. Hashing indicates 
that changes are assessed to be small compared to natural variations. Blank regions (if any) are 
where projections are assessed to be inconclusive (Appendix B). 


Projected Change (mm) in Soil Moisture, End of Century, Higher Emissions 
Winter Spring 


Summer 


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Chapter 8 


Projected Change (%) in Runoff, End of Century, Higher Emissions 
Winter Spring 




Summer 


1 

2 Figure 8.2. Projected end of the 21st century weighted CMIP5 multimodel average percent 

3 changes in total seasonal runoff (mrro) under the RCP8.5 scenario. Stippling indicates that 

4 changes are assessed to be large compared to natural variations. Hashing indicates that changes 

5 are assessed to be small compared to natural variations. Blank regions (if any) are where 

6 projections are assessed to be inconclusive (Appendix B). 

7 


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Chapter 8 



Historical 




0 80 160 240 

1 

2 

3 Figure 8.3. Projected changes in winter (DJF) Snow Water Equivalent at the middle and end of 

4 this century under the RCP8.5 scenario from a high-resolution version of the Community 

5 Atmospheric Model, CAM5 (Rhoades et al. 2016), Figure source: Lawrence Berkeley National 

6 Laboratory 

7 


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Chapter 8 


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5 Mountains Using Variable-Resolution CESM. Climate Dynamics, In press. 

6 Rupp, D.E., P.W. Mote, N. Massey, F.E.L. Otto, and M.R. Allen, 2013: Human influence on the 

7 probability of low precipitation in the central United States in 2012 [in "Explaining Extremes 

8 of 2013 from a Climate Perspective"] . Bulletin of the American Meteorological Society, 94 , 

9 S2-S6. http://dx.doi.org/10.1175/BAMS-D-13-00085T 

10 Seager, R., M. Hoerling, S. Schubert, H. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

1 1 Henderson, 2015: Causes of the 2011-14 California Drought. Journal of Climate, 28 , 6997- 

12 7024. http://dx.doi.Org/10.1175/JCLI-D-14-00860.l 

13 Seager, R., M. Hoerling, D.S. Siegfried, h. Wang, B. Lyon, A. Kumar, J. Nakamura, and N. 

14 Henderson, 2014: Causes and predictability of the 2011-14 California drought. 40 pp. NOAA 

15 Drought Task Force Narrative Team. http://cpo.noaa.gov/MAPP/califomiadroughtreport 

16 Sheffield, J., E.F. Wood, and M.L. Roderick, 2012: Little change in global drought over the past 

17 60 years. Nature, 491 , 435-438. http://dx.doi.org/10.1038/naturell575 

18 Swain, D., M. Tsiang, M. Haughen, D. Singh, A. Charland, B. Rajarthan, and N.S. Diffenbaugh, 

19 2014: The extraordinary California drought of 2013/14: Character, context and the role of 

20 climate change [in "Explaining Extremes of 2013 from a Climate Perspective"]. Bulletin of 

21 the American Meteorological Society, 95 , S3-S6. http://dx.doi.org/10T 175/1520-0477- 

22 95.9.S1.1 

23 Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. 

24 Wehner, J. Willis, D. Anderson, S. Doney, R. Feely, P. Hennon, V. Kharin, T. Knutson, F. 

25 Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Ch. 2: Our changing climate. 

26 Climate Change Impacts in the United States: The Third National Climate Assessment. 

27 Melillo, J.M., T.C. Richmond, and G.W. Yohe, Eds. U.S. Global Change Research Program, 

28 Washington, D.C., 19-67. http://dx.doi.org/10.7930/J0KW5CXT 

29 Wang, H., S. Schubert, R. Koster, Y.-G. Ham, and M. Suarez, 2014: On the Role of SST Forcing 

30 in the 2011 and 2012 Extreme U.S. Heat and Drought: A Study in Contrasts. Journal of 

31 Hydrometeorology , 15 , 1255-1273. http://dx.doi.org/10.1175/JHM-D-13-069T 

32 Wehner, M., D.R. Easterling, J.H. Lawrimore, R.R. Heim Jr, R.S. Vose, and B.D. Santer, 2011: 

33 Projections of future drought in the continental United States and Mexico. Journal of 

34 Hydrometeorology , 12 , 1359-1377. http://dx.doi.org/10.1175/2011JHM1351T 


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1 Williams, A.P., R. Seager, J.T. Abatzoglou, B.I. Cook, J.E. Smerdon, and E.R. Cook, 2015: 

2 Contribution of anthropogenic warming to California drought during 2012-2014. 

3 Geophysical Research Letters, 42, 6819-6828. http://dx.doi.org/10.1002/2015GL064924 

4 Williams, I.N. and M.S. Torn, 2015: Vegetation controls on surface heat flux partitioning, and 

5 land-atmosphere coupling. Geophysical Research Letters, 42, 9416-9424. 

6 http ://dx .doi .org / 10.1 002/20 1 5GL066305 

7 Woodhouse, C.A., S.T. Gray, and D.M. Meko, 2006: Updated streamflow reconstructions for the 

8 Upper Colorado River Basin. Water Resources Research, 42. 

9 http://dx.doi.org/10.1029/2005WR004455 

10 Yoon, J.-H., B. Kravitz, PJ. Rasch, S.-Y.S. Wang, R.R. Gillies, and L. Hipps, 2015: Extreme 

1 1 Fire Season in California: A Glimpse Into the Future? Bulletin of the American 

12 Meteorological Society, 96, S5-S9. http://dx.doi.Org/10.1175/bams-d-15-00114.l 

1 3 http://joumals .ametsoc .org/doi/abs/10 . 117 5/B AMS-D- 15-00 114.1 


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1 9. Extreme Storms 

2 KEY FINDINGS 

3 1 . Human activities have contributed substantially to observed ocean-atmosphere variability in 

4 the Atlantic Ocean (i medium confidence), and these changes have contributed to the observed 

5 increasing trend in North Atlantic hurricane activity since the 1970s ( medium confidence). 

6 2. For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, 

7 increases are projected in precipitation rates (high confidence) and intensity (medium 

8 confidence) . The frequency of the most intense of these storms is projected to increase in the 

9 Atlantic and western North Pacific (low confidence) and in the eastern North Pacific ( medium 

10 confidence). 

11 3. Tornado activity in the United States has become more variable, particularly over the 2000s, 

12 with a decrease in the number of days per year experiencing tornadoes, and an increase in the 

13 number of tornadoes on these days ( high confidence). Confidence in past trends for hail and 

14 severe thunderstorm winds, however, is low. Climate models consistently project 

15 environmental changes that would putatively support an increase in the frequency and 

16 intensity of severe thunderstorms (a category that combines tornadoes, hail, and winds), 

17 especially over regions that are currently prone to these hazards, but confidence in the details 

18 of this increase is low. 

19 4. There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency on 

20 the southern margins of climatologically snowy areas ( medium confidence). Winter storm 

21 tracks have shifted northward since 1950 over the Northern Hemisphere (medium 

22 confidence). Projections of winter storm frequency and intensity over the United States vary 

23 from increasing to decreasing depending on region, but model agreement is poor and 

24 confidence is low. Potential linkages between the frequency and intensity of severe winter 

25 storms in the United States and accelerated warming in the Arctic have been postulated, but 

26 they are complex, and, to some extent, controversial, and confidence in the connection is 

27 currently low. 

28 5. The frequency and severity of landfalling “atmospheric rivers” on the U. S. West Coast 

29 (narrow streams of moisture that account for 30%-40% of precipitation and snowpack in the 

30 region and are associated with severe flooding events) will increase as a result of increasing 

3 1 evaporation and resulting higher atmospheric water vapor that occurs with increasing 

32 temperature. (Medium confidence) 

33 9.1 Introduction 

34 Quantifying how broad-scale average climate influences the behavior of extreme storms is 

35 particularly challenging, in part because extreme storms are comparatively short-lived events and 


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1 occur within an environment of largely random variability. Additionally, because the physical 

2 mechanisms linking climate change and extreme storms can manifest in a variety of ways, even 

3 the sign of the changes in the extreme storms can vary in a warming climate. This makes 

4 detection and attribution of trends in extreme stonn characteristics more difficult than detection 

5 and attribution of trends in the larger environment in which the storms evolve (e.g., Ch. 6: 

6 Temperature Change). Despite the challenges, good progress is being made for a variety of storm 

7 types, such as tropical cyclones, severe convective storms (thunderstorms), winter storms, and 

8 atmospheric river events. 

9 9.2 Tropical Cyclones (Hurricanes, Typhoons) 

10 Detection and attribution (Ch. 3: Detection and Attribution) of past changes in tropical cyclone 

1 1 (TC) behavior remain a challenge due to the nature of the historical data, which are highly 

12 heterogeneous in both time and among the various regions that collect and analyze the data 

13 (Kossin et al. 2013; Klotzbach and Landsea 2015; Walsh et al. 2016). While there are ongoing 

14 efforts to reanalyze and homogenize the data (e.g., Landsea et al. 2015; Kossin et al. 2013), there 

15 is still low confidence that any reported long-term (multidecadal to centennial) increases in TC 

16 activity are robust, after accounting for past changes in observing capabilities (which is 

17 unchanged from the IPCC AR5 assessment statement [Hartmann et al. 2013]). This is not meant 

18 to imply that no such increases have occurred, but rather that the data are not of a high enough 

19 quality to determine this with much certainty. 

20 Both theory and numerical modeling simulations (in general) indicate an increase in TC intensity 

21 in a wanner world, and the models generally show an increase in the number of very intense TCs 

22 (Bindoff et al. 2013; Christensen et al. 2013; Walsh et al. 2015; Knutson et al. 2015). In some 

23 cases, climate models can be used to make attribution statements about TCs without formal 

24 detection (see also Ch. 3: Detection and Attribution). For example, there is evidence that, in 

25 addition to the effects of El Nino, anthropogenic forcing made the extremely active 2014 

26 Hawaiian hurricane season substantially more likely, although no significant rising trend in TC 

27 frequency near Hawaifi was detected (Murakami et al. 2015). 

28 Changes in frequency and intensity are not the only measures of TC behavior that may be 

29 affected by climate variability and change, and there is evidence that the locations where TCs 

30 reach their peak intensity has migrated poleward over the past 30 years in the Northern and 

3 1 Southern Hemispheres, apparently in concert with environmental changes associated with the 

32 independently observed expansion of the tropics (Kossin et al. 2014). The poleward migration in 

33 the western North Pacific (Kossin et al. 2016), which includes a number of United States 

34 Territories, appears particularly robust and remains significant over the past 60-70 years after 

35 accounting for the known modes of natural variability in the region (Figure 9.1). The migration, 

36 which can substantially change patterns of TC hazard exposure and mortality risk, is also evident 

37 in 2 1st century Coupled Model Intercomparison Project Phase 5 (CMIP5) projections following 

38 the RCP8.5 emissions trajectories, suggesting a possible link to human activities. Further 


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analysis comparing observed past TC behavior with climate model historical forcing runs (and 
with model control runs simulating multidecadal internal climate variability alone) are needed to 
better understand this process, but it is expected that this will be an area of heightened future 
research. 


[INSERT FIGURE 9.1 HERE: 


Figure 9.1: Poleward migration, in degrees of latitude, of the location of annual-mean TC 


lifetime intensity in the western N. Pacific ocean, after accounting for the known regional modes 

of interannual (El Nino-Southern Oscillation; ENSO) and interdecadal (Pacific Decadal 
Oscillation; PDO) variability. The time series shows residuals of the multivariate regression of 


annually-averaged latitude of TC peak lifetime intensity onto the mean Nino-3. 4 and PDO 


indices. Data are taken from the Joint Typhoon Warning Center (JTWC). Shading shows 95% 
confidence bounds for the trend. Annotated values at lower-r ight show the mean migration rate 
and its 95% confidence interval in degrees per decade for the period 1945-2013. (Figure source: 
redrawn from Kossin et al. 2016; © American Meteorological Society. Used with permission.)] 

In the Atlantic, observed multidecadal variability of the ocean and atmosphere, which TCs are 
shown to respond to, has been attributed (Ch. 3: Detection and Attribution) to natural internal 
variability via meridional overturning ocean circulation changes (Delworth and Mann 2000), 
natural external variability caused by volcanic eruptions (Thompson and Solomon 2009; Evan 
2012) and Saharan dust outbreaks (Evan et al. 2009, 2011), and anthropogenic external forcing 
via greenhouse gases and sulfate aerosols (Mann and Emanuel 2006; Booth et al. 2012; 

Dunstone et al. 2013). Determining the relative contributions of each mechanism to the observed 
multidecadal variability in the Atlantic, and even whether natural or anthropogenic factors have 
dominated, is presently a very active area of research and debate, and no consensus has yet been 
reached (Carslaw et al. 2013; Zhang et al. 2013; Tung and Zhao 2013; Mann et al. 2014; Stevens 
2015; Sobel et al. 2016). Despite the level of disagreement about the relative magnitude of 
human influences, there is broad agreement in the literature that human factors have had a 
measurable impact on the observed oceanic and atmospheric variability in the North Atlantic, 
and there is medium confidence that this has contributed to the observed increase in hurricane 
activity since the 1970s. This is essentially unchanged from the Intergovernmental Panel on 
Climate Change Fifth Assessment Report (IPCC AR5) statement (Bindoff et al. 2013), although 
the post-AR5 literature has only served to further support this statement (Kossin et al. 2015). 
This is expected to remain an active research topic in the foreseeable future. 


The IPCC AR5 consensus TC projections for the late 21st century (IPCC Figure 14.17; 
Christensen et al. 2013) include an increase in global mean TC intensity, precipitation rate, and 
frequency of very intense (Saffir-Simpson Category 4-5) TCs, and a decrease, or little change, in 
global tropical cyclone frequency. Since the IPCC AR5, some studies have provided additional 
support for this consensus, and some have challenged it. For example, a recent study (Fig. 9.2) 
projects increased mean TC intensity and occurrence of Saffir-Simpson Category 4-5 storms in 
the Atlantic Ocean basin and in most, but not all, other TC-supporting basins (Knutson et al. 


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2015). However, another recent (post-AR5) study proposed that increased thermal stratification 
of the upper ocean in CMIP5 climate warming scenarios should substantially reduce the 
warming-induced intensification of TCs estimated in previous studies (Huang et al. 2015). 
Follow-up studies, however, estimate that the effect of such increased stratification is relatively 
small, reducing the projected intensification of TCs by only about 1 0%— 1 5% (Emanuel 2015; 
Tuleya et al. 2016). 

Another recent study challenged the IPCC AR5 consensus projections by simulating increased 
global TC frequency over the 21st century under the RCP8.5 scenario (Emanuel 2013). 
However, another modeling study has found that neither direct analysis of CMIP5-class 
simulations, nor indirect inferences from the simulations (such as those of Emanuel 2013), could 
reproduce the sign of the change in TC frequency projected in a warmer world by high- 
resolution TC-permitting climate models (Wehner et al. 2015), which adds uncertainty to the 
results of Emanuel (2013). 

In summary, despite new research that challenges some aspects of the AR5 consensus for late 
21st century projected TC activity, it remains likely that global mean tropical cyclone maximum 
wind speeds and precipitation rates will increase; and it is more likely than not that the global 
frequency of occurrence of TCs will either decrease or remain essentially the same. Confidence 
in projected global increases of intensity and tropical cyclone precipitation rates is medium and 
high, respectively, as there is some consistency among studies and at least a fair degree of 
consensus. Confidence in projected increases in the frequency of very intense TCs is generally 
lower (: medium in the eastern North Pacific and low in the western North Pacific and Atlantic) 
due to comparatively fewer studies available and due to the competing influences of projected 
reductions in overall storm frequency and increased mean intensity on the frequency of the most 
intense storms. Both the magnitude and sign of projected changes in individual ocean basins 
appears to depend on the large-scale pattern of changes to atmospheric circulation and ocean 
surface temperature (e.g., Knutson et al. 2015). Projections of these regional patterns of change 
— apparently critical for TC projections — are uncertain, leading to uncertainty in regional TC 
projections. 

[INSERT FIGURE 9.2 HERE: 

Figure 9.2: Simulated occurrence of tropical cyclones of at least Category 4 intensity (surface 
winds of at least 59 m/s [132 mph]) for (a) present-day or (b) late-twenty-first-century (RCP4.5; 
CMIP5 multimodel ensemble) conditions; unit: storms per decade. Simulated tropical cyclone 

tracks were obtained using the GFDL hurricane model to resimulate (at higher resolution) the 

tropical cyclone cases originally obtained from the HiRAM Cl 80 global mode. Occurrence 

refers to the number of days, over a 20-year period, in which a stonn exceeding 59 m/s (132 

mph) intensity was centered within the 10° x 10° grid region, (c) Difference in occurrence rate 
between late twenty- first century and present day [(b) minus (a)]. White regions are regions 
where no tropical storms occurred in the simulations [in (a) and (b)] or where the difference 


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between the experiments is zero [in (c)]. (Figure source: redrawn from Knutson et al. 2015; © 
American Meteorological Society. Used with permission.).] 

START BOX 9.1 HERE 


Box 9.1: U.S. Landfalling Major Hurricane “Drought” 

The last major hurricane (Saffir-Simpson Category 3 or higher) to make landfall in the 
continental United States was Wilma in 2005. The current 1 1-year (2006-2016) absence of U.S. 
major hurricane landfall events (sometimes colloquially referred to as a “hurricane drought”) is 
unprecedented in the historical records dating back to the mid- 19th century, and has occurred in 
tandem with average to above-average basin-wide major hurricane counts. Is the absence of U.S. 
landfalling major hurricanes due to random luck, or are there systematic changes in climate 
driving this? 

One recent study indicates that the absence of U.S. landfalling major hurricanes cannot readily 
be attributed to any sustained changes in the climate patterns that affect hurricanes (Hall and 
Heried 2015). Based on a statistical analysis of the historical North Atlantic hurricane database, 
the study found no evidence for memory in major U.S. landfalls from one year to the next and 
concluded that the 1 1-year absence of U.S. landfalling major hurricanes is random. Another 
recent study did identify a systematic pattern of atmosphere/ocean conditions that vary in such a 
way that conditions conducive to hurricane intensification in the deep tropics occur in concert 
with conditions conducive to weakening near the U.S. coast (Kossin 2016). This result suggests a 
possible relationship between climate and hurricanes; increasing basin-wide hurricane counts are 
associated with decreasing fraction of major hurricanes making U.S. landfall, as major 
hurricanes approaching the U.S. coast are more likely to weaken during active North Atlantic 
hurricane periods (such as the present period). It is unclear to what degree this relationship has 
affected absolute hurricane landfall counts during the recent active hurricane period from the 
mid-1990s, as the basin-wide number and landfalling fraction are in opposition (that is, there are 
more major hurricanes but a smaller fraction make landfall as major hurricanes). It is also 
unclear how this relationship may change as the climate continues to warm. 

A third recent study (Hart et al. 2016) shows that the extent of the absence is sensitive to 
uncertainties in the historical data and even small variations in the definition of a major 
hurricane, which is somewhat arbitrary. It is also sensitive to the definition of U.S. landfall, 
which is a geopolitical-border-based constraint and has no physical meaning. In fact, many areas 
outside of the U.S. border have experienced major hurricane landfalls in the past 1 1 years. In this 
sense, the frequency of U.S. landfalling major hurricanes is not a particularly robust metric with 
which to study questions about hurricane activity and its relationship with climate variability. 
Furthermore, the 1 1-year absence of U.S. landfalling major hurricanes is not a particularly 
relevant metric in terms of coastal hazard exposure and risk. For example, Hurricanes Ike (2008), 
Irene (2011), and Sandy (2012), and most recently Hurricane Matthew (2016) brought severe 


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impacts to the U.S. coast despite not making landfall in the United States as major hurricanes. In 
the case of Hurricane Matthew, the center came within about 40 miles of the Florida coast while 
Matthew was a major hurricane, which is close enough to significantly impact the coast but not 
close enough to break the “drought” as it’s defined. 

In summary, the 1 1-year absence of U.S. landfalling major hurricanes is anomalous. There is 
some evidence that systematic atmosphere/ocean variability has reduced the fraction of 
hurricanes making U.S. landfall since the mid-1990s, but this is at least partly countered by 
increased basin-wide numbers, and the net effect on landfall rates is unclear. Moreover, there is a 
large random element, and the metric itself suffers from lack of physical basis due to the 
arbitrary intensity threshold and geopolitically based constraints. Additionally, U.S. coastal risk, 
particularly from storm surge and freshwater flooding, depends strongly on storm size, 
propagation speed and direction, and rainfall rates. There is some danger, in the form of evoking 
complacency, in placing too much emphasis on the recent absence of a specific subset of 
hurricanes. 

END BOX 9.1 HERE 

9.3 Severe Convective Storms (Thunderstorms) 

Tornado and severe thunderstorm events cause significant loss of life and property: more than 
one -third of the $ 1 billion weather disasters in the United States during the past 25 years were 
due to such events, and relative to other extreme weather, the damages from convective weather 
hazards have undergone the largest increase (Smith and Katz 2013). A particular challenge in 
quantifying the existence and intensity of these events arises from the data source: rather than 
measurements, the occurrence of tornadoes and severe thunderstorms is determined by visual 
sightings by eyewitnesses (such as “storm spotters” and law enforcement officials). The 
reporting has been susceptible to changes in population density, modifications to reporting 
procedures and training, the introduction of video and social media, and so on. These have led to 
systematic, non-meteorological biases in the long-tenn data record. 

Nonetheless, judicious use of the report database has revealed important information about 
tornado trends. Since the 1970s, the United States has experienced a decrease in the number of 
days per year on which tornadoes occur, but an increase in the number of tornadoes that form on 
such days (Brooks et al. 2014). One important implication is that the frequency of days with 
large numbers of tornadoes — tornado outbreaks — appears to be increasing (Figure 9.3). The 
extent of the season over which such tornado activity occurs is increasing as well: although 
tornadoes in the United States are observed in all months of the year, an earlier calendar-day start 
to the season of high activity is emerging. In general, there is more interannual variability, or 
volatility, in tornado occurrence (Tippett 2014: see also Eisner et al. 2015). 


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[INSERT FIGURE 9.3 HERE: 

Figure 9.3: Annual tornado activity in the United States over the period 1955-2013. The black 

squares indicate the number of days per year with at least one tornado rated (E)F 1 or greater, and 

the black circles and line show the decadal mean line of such tornado days. The red triangles 

indicate the number of days per year with more than 30 tornadoes rated (E)F1 or greater, and the 

red circles and line show the decadal mean of these tornado outbreaks. (Figure source: redrawn 

from Brooks et al. 2014)] 


Evaluations of hail and (non-tomadic) thunderstonn wind reports have thus far been less 
revealing. Although there is evidence of an increase in the number of hail days per year, the 
inherent uncertainty in reported hail size reduces the confidence in such a conclusion (Allen and 
Tippett 2015). Thunderstonn wind reports have proven to be even less reliable, because, as 
compared to tornadoes and hail, there is less tangible visual evidence; thus, although the United 
States has lately experienced several significant thunderstonn wind events (sometimes referred 
to as “derechos”), the lack of studies that explore long-term trends in wind events and the 
uncertainties in the historical data preclude any robust assessment. 

It is possible to bypass the use of reports by exploiting the fact that the temperature, humidity, 
and wind in the larger vicinity — or “environment” — of a developing thunderstorm ultimately 
control the intensity, morphology, and hazardous tendency of the storm. Thus, the premise is that 
quantifications of the vertical profiles of temperature, humidity, and wind that can be used as a 
proxy for actual severe thunderstonn occurrence. In particular, a thresholded product of 
convective available potential energy (CAPE) and vertical wind shear over a surface-to-6 km 
layer (S06) constitutes one widely used means of representing the frequency of severe 
thunderstorms (Brooks et al. 2003). This environmental-proxy approach avoids the biases and 
other issues with eyewitness storm reports and is readily evaluated using the relatively coarse 
global data sets and global climate models. It has the disadvantage of assuming that a 
thunderstonn will necessarily form and then realize its environmental potential. 

Upon employing Global Climate Models (GCMs) to evaluate CAPE and S06, a consistent 
finding among a growing number of proxy-based studies is a projected increase in the frequency 
of severe thunderstorm environments in the United States over the mid- to late 21st century (Van 
Klooster and Roebber 2009; Diffenbaugh et al. 2013; Gensini et al. 2014; Seely and Romps 
2015). The most robust projected increases in frequency are over the central United States, 
during March- April-May (MAM) (Diffenbaugh et al. 2013). Based on the increased frequency of 
very high CAPE, increases in storm intensity are also projected over this same period (see also 
Del Genio et al. 2007). 

Key limitations of the environmental proxy approach are being addressed through the 
applications of high-resolution dynamical downscaling, wherein sufficiently fine model grids are 
used so that individual thunderstorms are explicitly resolved, rather than implicitly represented 
(as through environmental proxies). The individually modeled thunderstorms can then be 


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1 quantified and assessed in terms of severity (Trapp et al. 2011; Robinson et al. 2013; Gensini and 

2 Mote 2014). A comprehensive approach using a dynamically downscaled GCM over 30-year 

3 historical and future climate periods showed the following: 1) a relatively large increase in the 

4 severe thunderstonn occurrence during the early part of MAM within the southeastern United 

5 States; 2) a northward and eastward expansion of the occurrence frequency, especially during 

6 MAM; and 3) a significant increase in the frequency in June -July-August (JJA), particularly in 

7 the northern Great Plains (Hoogewind et al. 2016). 

8 The computational expense of high-resolution dynamical downscaling makes it difficult to 

9 generate model ensembles over long time periods, and thus to assess the uncertainty of the 

10 downscaled projections. Because these dynamical downscaling implementations focus on the 

1 1 statistics of stonn occurrence rather than on faithful representations of individual events, they 

12 have generally been unconcerned with specific extreme convective events in history. So, for 

13 example, such downscaling does not address whether the intensity of an event like the Joplin, 

14 Missouri, tornado of May 22, 2011, would be amplified under projected future climates. 

15 Recently, the “pseudo-global warming” (PGW) methodology (see Schar et al. 1996), which is a 

16 variant of dynamical downscaling, has been adapted to address these and related questions. As an 

17 example, when the parent “supercell” of select historical tornado events forms under the climate 

18 conditions projected during the late 21st century, it becomes a more intense supercell rather than 

19 a benign, unorganized thunderstorm (Trapp and Hoogewind 2016). The intensity and, by 

20 extension, the severity of these supercells fall short of the expectations based on CAPE. 

2 1 However, the updrafts simulated under PGW are relatively more intense, but not in proportion to 

22 the projected higher levels of CAPE. 

23 9.4 Winter Storms 

24 The frequency of large snowfall years has decreased in the southern United States and Pacific 

25 Northwest and increased in the northern United States (see Ch. 7: Precipitation Change). The 

26 winters of 2013/2014 and 2014/2015 have contributed to this trend. They were characterized by 

27 frequent storms and heavier-than-nonnal snowfalls in the Midwest and Northeast and drought in 

28 the western United States. These were related to blocking (a large-scale pressure pattern with 

29 little or no movement) of the wintertime circulation in the Pacific sector of the Northern 

30 Hemisphere (e.g., Marinaro et al. 2015) that put this part of the United States in the primary 

3 1 winter storm track, while at the same time reducing the number of winter storms in California, 

32 causing severe drought conditions (Chang et al. 2015). While some observational studies suggest 

33 a linkage between blocking affecting the U.S. climate and enhanced Arctic wanning (arctic 

34 amplification), specifically for an increase in highly amplified jet stream patterns in winter over 

35 the United States (Francis and Skific 2015), other studies show mixed results (Bames and 

36 Polvani 2015; Perlwitz et al. 2015; Screen et al. 2015). Therefore, a definitive understanding of 

37 the effects of arctic amplification on midlatitude winter weather remains elusive, and other 


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explanations have been offered for the weather patterns of recent winters, such as anomalously 
strong Pacific trade winds (Yang et al. 2015). 

Analysis of storm tracks indicates that there has been an increase in winter storm frequency and 
intensity since 1950, with a slight shift in tracks toward the poles (Wang et al. 2006, 2012; Vose 
et al. 2014). Current global climate models (CMIP5) do in fact predict an increase in 
extratropical cyclone (ETC) frequency over the eastern United States, including the most intense 
ETCs, under the high RCP8.5 emission scenario (Colle et al. 2013). However, there are large 
model-to-model differences in the realism of ETC simulations and in the projected changes. 
Moreover, projected ETC changes have large regional variations, including a decreased total 
frequency in the North Atlantic, further highlighting the complexity of the response to climate 
change. 

9.5 Atmospheric Rivers 

The tenn “atmospheric rivers” (ARs) refers to the relatively narrow streams of moisture transport 
that often occur within and across midlatitudes (Zhu and Newell 1998) (Figure 9.4), in part 
because they often transport as much water as in the Amazon River (Newell et al. 1992). While 
ARs occupy less than 10% of the circumference of the Earth at any given time, they account for 
90% of the poleward moisture transport across midlatitudes. In many regions of the world, they 
account for a substantial fraction of the precipitation (Guan and Waliser 2015), and thus water 
supply, often delivered in the form of an extreme weather and precipitation event (Figure 9.4). 
For example, ARs account for 30%-40% of the typical snow pack in the Sierra Nevada 
mountains and annual precipitation in the U.S. West Coast states (Guan et al. 2010; Dettinger et 
al. 2011) — an essential summertime source of water for agriculture, consumption, and ecosystem 
health. However, this vital source of water is also associated with severe flooding, with 
observational evidence showing a close connection between historically high streamflow events 
and floods with landfalling AR events, in the west and other sectors of the United States (Ralph 
et al. 2006; Neiman et al. 2011; Moore et al. 2012). More recently, research has also 
demonstrated that ARs are often found to be critical in ending droughts in the western United 
States (Dettinger 2013). 

[INSERT FIGURE 9.4 HERE: 

Figure 9.4: (upper left) Atmospheric rivers depicted in Special Sensor Microwave Imager 
(SSM/I) measurements of total column water vapor leading to extreme precipitation events at 

landfall locations, (upper right) Annual mean frequency of atmospheric river occurrence (for 
example, 12% means about 1 every 8 days) and their integrated moisture transport (IVT) (Guan 
and Waliser 2015). (lower left) ARs are the dominant synoptic storms for the U.S. west coast in 

terms of extreme precipitation (Ralph and Dettinger 2012) and (lower right) supply a large 
fraction of the annual precipitation in the U.S. west coast states (Dettinger et al. 2011). [Figure 
source: (upper left) Ralph et al. 2011, (upper right) Guan and Waliser 2015, (lower left) Ralph 


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and Dettinger 2012, (lower right), Dettinger et al. 201 1; left panels, © American Meteorological 

Society. Used with permission.]] 


Given the important role that ARs play in the water supply of the western United States and their 
role in weather and water extremes in the west and occasionally other parts of the United States 
(e.g., Rutz et al. 2014), it is critical to examine how climate change and the expected 
intensification of the global water cycle and atmospheric transports (e.g., Held and Soden 2006; 
Lavers et al. 2015) are projected to impact ARs (e.g., Dettinger and Ingram 2013). Under climate 
change conditions, ARs may be altered in a number of ways, namely their frequency, intensity, 
duration, and locations. In association with landfalling ARs, any of these would be expected to 
result in impacts on hazards and water supply given the discussion above. Assessments of ARs in 
climate change projections for the United States have been undertaken for central California 
from CMIP3 (Dettinger et al. 2011) and a number of studies for the West Coast of North 
America (Warner et al. 2015;_Payne and Magnusdottir 2015; Gao et al. 2015; Radio et al. 2015; 
Hagos et al. 2016), and these studies have uniformly shown that ARs are likely to become more 
frequent and intense in the future. For example, one recent study reveals a large increase of AR 
days along the West Coast by the end of the 21st century in the RCP8.5 scenario, with fractional 
increases between 50% and 600%, depending on the seasons and landfall locations (Gao et al. 
2015). Results from these studies (and Lavers et al. 2013 for ARs impacting the United 
Kingdom) show that these AR changes were predominantly driven by increasing atmospheric 
specific humidity, with little discernible change in the low-level winds. The higher atmospheric 
water vapor content in a warmer climate is to be expected because of an increase in saturation 
water vapor pressure with air temperature (Ch. 2: Scientific Basis). While the thermodynamic 
effect appears to dominate the climate change impact on ARs, leading to projected increases in 
ARs, there is evidence for a dynamical effect (that is, location change) related to the projected 
poleward shift of the subtropical jet that diminished the thermodynamic effect in the southern 
portion of the West Coast of North America (Gao et al. 2015). 

The evidence for considerable increases in the number and intensity of ARs depends (as do all 
climate changes studies based on dynamical models) on the model fidelity in representing ARs 
and their interactions with the global climate/circulation. Additional confidence comes from 
studies that show qualitatively similar increases while also providing evidence that the models 
represent AR frequency, transports, and spatial distributions relatively well compared to 
observations (Payne and Magnusdottir 2015; Hagos et al. 2016). A caveat associated with 
drawing conclusions from any given study or differences between two is that they typically use 
different detection methodologies that are typically tailored to a regional setting (cf. Guan and 
Waliser 2015). Additional research is warranted to examine these storms from a global 
perspective, with additional and more in-depth process-oriented diagnostics/metrics. Stepping 
away from the sensitivities associated with defining atmospheric rivers, one study examined the 
intensification of the integrated vapor transport (IVT), easily and unambiguously defined (Lavers 
et al. 2015). That study found that for the RCP8.5 scenario, multimodel mean IVT and the IVT 


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1 associated with extremes above 95% percentile increase by 30%-40% in the North Pacific. 

2 These results, along with the uniform findings of the studies above examining projected changes 

3 in ARs for the western North America and the United Kingdom, give high confidence that the 

4 frequency of AR storms will increase in association with rising global temperatures. 

5 


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Chapter 9 


1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 Human activities have contributed substantially to observed ocean-atmosphere variability in the 

4 Atlantic Ocean ( medium confidence), and these changes have contributed to the observed 

5 increasing trend in North Atlantic hurricane activity since the 1970s ( medium confidence). 

6 Description of evidence base 

7 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

8 science literature and are similar to statements made in previous national (NCA3; Melillo et ah, 

9 2014) and international (IPCC 2013) assessments. Data limitations are documented in Kossin et 

10 al. (2013) and references therein. Contributions of natural and anthropogenic factors in observed 

1 1 multidecadal variability are quantified in Carslaw et al. 2013; Zhang et al. 2013; Tung and Zhao 

12 2013; Mann et al. 2014; Stevens 2015; Sobel et al. 2016; Walsh et al. 2015. 

13 Major uncertainties 

14 Key remaining uncertainties are due to known and substantial heterogeneities in the historical 

15 tropical cyclone data and lack of robust consensus in determining the precise relative 

16 contributions of natural and anthropogenic factors in past variability of the tropical environment. 

17 Assessment of confidence based on evidence and agreement, including short description of 

18 nature of evidence and level of agreement 

19 □ Very High 

20 □ High 

21 X Medium 

22 □ Low 

23 Although the range of estimates of natural versus anthropogenic contributions in the literature is 

24 fairly broad, virtually all studies identify a measurable, and generally substantial, anthropogenic 

25 influence. This does constitute a consensus for human contribution to the increases in tropical 

26 cyclone activity since 1970. 

27 Summary sentence or paragraph that integrates the above information 

28 The key message and supporting text summarizes extensive evidence documented in the climate 

29 science peer-reviewed literature. The uncertainties and points of consensus that were described 

30 in the NCA3 and IPCC assessments have continued. 

31 

32 Key Finding 2 

33 For Atlantic and eastern North Pacific hurricanes and western North Pacific typhoons, increases 

34 are projected in precipitation rates ( high confidence) and intensity (medium confidence). The 

35 frequency of the most intense of these storms is projected to increase in the Atlantic and western 

36 North Pacific (low confidence) and in the eastern North Pacific ( medium confidence). 


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1 Description of evidence base 

2 The Key Finding and supporting text summarizes extensive evidence documented in the climate 

3 science literature and are similar to statements made in previous national (NCA3; Melillo et al. 

4 2014) and international (IPCC 2013) assessments. Since these assessments, more recent 

5 downscaling studies have further supported these assessments (e.g., Knutson et al. 2015), though 

6 pointing out that the changes (future increased intensity and tropical cyclone precipitation rates) 

7 may not occur in all basins. 

8 Major uncertainties 

9 A key uncertainty remains the lack of a supporting detectable anthropogenic signal in the 

10 historical data to add further confidence to these projections. As such, confidence in the 

1 1 projections is based on agreement among different modeling studies and physical understanding 

12 (for example, potential intensity theory for tropical cyclone intensities and the expectation of 

13 stronger moisture convergence, and thus higher precipitation rates, in tropical cyclones in a 

14 warmer environment containing greater amounts of environmental atmospheric moisture). 

15 Additional uncertainty stems from uncertainty in both the projected pattern and magnitude of 

16 future sea surface temperatures (Knutson et al. 2015). 

17 Assessment of confidence based on evidence and agreement, including short description of 

18 nature of evidence and level of agreement 

19 □ Very High 

20 xn High 

21 xn Medium 

22 xn Low 

23 Confidence is rated as high in tropical cyclone rainfall projections and medium in intensity 

24 projections since there are a number of publications supporting these overall conclusions, fairly 

25 well established theory, generally consistency among different studies, varying methods used in 

26 studies, and still a fairly strong consensus among studies. However, a limiting factor for 

27 confidence in the results is the lack of a supporting detectable anthropogenic contribution in 

28 observed tropical cyclone data. 

29 There is low to medium confidence for increased occurrence of the most intense tropical cyclones 

30 for most basins, as there are relatively few formal studies that focus on these changes, and the 

3 1 change in occurrence of such storms would be enhanced by increased intensities, but reduced by 

32 decreased overall frequency of tropical cyclones. 

33 Summary sentence or paragraph that integrates the above information 

34 Models are generally in agreement that tropical cyclones will be more intense and have higher 

35 precipitation rates, at least in most basins. Given the agreement between models and support of 

36 theory and mechanistic understanding, there is medium to high confidence in the overall 

37 projection, although there is some limitation on confidence levels due to the lack of a supporting 

38 detectable anthropogenic contribution to tropical cyclone intensities or precipitation rates. 


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Chapter 9 


1 Key Finding 3 

2 Tornado activity in the United States has become more variable, particularly over the 2000s, with 

3 a decrease in the number of days per year experiencing tornadoes, and an increase in the number 

4 of tornadoes on these days ( high confidence). Confidence in past trends for hail and severe 

5 thunderstonn winds, however, is low. Climate models consistently project environmental 

6 changes that would putatively support an increase in the frequency and intensity of severe 

7 thunderstorms (a category that combines tornadoes, hail, and winds), especially over regions that 

8 are currently prone to these hazards, but confidence in the details of this increase is low. 

9 Description of evidence base 

10 Evidence for the first and second statement comes from the U.S. database of tornado reports. 

1 1 There are well known biases in this database, but application of an intensity threshold (> a rating 

12 of 1 on the [Enhanced] Fujita scale) and the quantification of tornado activity in terms of tornado 

13 days instead of raw numbers of reports are thought to reduce these biases. It is not known at this 

14 time whether the variability and trends are necessarily due to climate change. 

15 The third statement is based on projections from a wide range of climate models, including 

16 GCMs and RCMs, mn over the past 10 years (e.g., see the review by Brooks 2013). The 

17 evidence is derived from an “environmental-proxy” approach, which herein means that severe- 

18 thunderstonn occurrence is related to the occurrence of two key environmental parameters, 

19 CAPE and vertical wind shear. A limitation of this approach is the assumption that the 

20 thunderstonn will necessarily form and then realize its environmental potential. This assumption 

21 is indeed violated, albeit at levels that vary by region and season. 

22 Major uncertainties 

23 Regarding the first and second statements, there is still some uncertainty in the database, even 

24 when the data are filtered. The major uncertainty in the third statement equates to the 

25 aforementioned limitation (that is, the thunderstonn will necessarily form and then realize its 

26 environmental potential). 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement 

29 High : That the variability in tornado activity has increased. 

30 Medium : That the severe-thunderstorm environmental conditions will change with a changing 

3 1 climate, but 

32 Low. on the precise (geographical and seasonal) realization of the environmental conditions as 

33 actual severe thunderstorms. 

34 Summary sentence or paragraph that integrates the above information 

35 Analyses and projections of tornado and severe thunderstorm trends depend on careful 

36 treatments of the historical record and on novel approaches on the use of climate model 

37 simulations. 


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1 Key Finding 4 

2 There has been a trend toward earlier snowmelt and a decrease in snowstorm frequency on the 

3 southern margins of climatologically snowy areas ( medium confidence). Winter storm tracks 

4 have shifted northward since 1950 over the Northern Hemisphere (/ medium confidence ). 

5 Projections of winter stonn frequency and intensity over the United States vary from increasing 

6 to decreasing depending on region, but model agreement is poor and confidence is low. Potential 

7 linkages between the frequency and intensity of severe winter storms in the United States and 

8 accelerated warming in the Arctic have been postulated, but they are complex, and, to some 

9 extent, controversial, and confidence in the connection is currently low. 

10 Description of evidence base 

1 1 The Key Finding and supporting text summarizes evidence documented in the climate science 

12 literature. 

13 Evidence for changes in winter storm track changes are documented in a small number of studies 

14 (Wang et al. 2006, 2012). Future changes are documented in one study (Colle et al. 2013), but 

15 there are large model-to-model differences. The effects of arctic amplification on U.S. winter 

16 stonns have been studied, but the results are mixed (Francis and Skific 2015; Barnes and Polvani 

17 2015; Perlwitz et al. 2015; Screen et al. 2015), leading to considerable uncertainties. 

18 Major uncertainties 

19 Key remaining uncertainties relate to the sensitivity of observed snow changes to the spatial 

20 distribution of observing stations, and to historical changes in station location and observing 

21 practices. There is conflicting evidence about the effects of arctic amplification on CONUS 

22 winter weather. 

23 Assessment of confidence based on evidence and agreement, including short description of 

24 nature of evidence and level of agreement 

25 Very High 

26 xlH High 

27 x Medium 

28 xlH Low 

29 There is high confidence that wanning has resulted in earlier snowmelt and decreased snowfall 

30 on the wann margins of areas with consistent snowpack based on a number of observational 

3 1 studies. There is medium confidence that Northern Hemisphere storm tracks have shifted north 

32 based on a small number of studies. There is low confidence in future changes in winter stonn 

33 frequency and intensity based on conflicting evidence from analysis of climate model 

34 simulations. 

35 Summary sentence or paragraph that integrates the above information 

36 Decreases in snowfall on southern and low elevation margins of cunently climatologically 

37 snowy areas are likely but winter storm frequency and intensity changes are uncertain. 


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1 Key Finding 5 

2 The frequency and severity of landfalling “atmospheric rivers” on the U. S. West Coast (narrow 

3 streams of moisture that account for 30%-40% of precipitation and snowpack in the region and 

4 are associated with severe flooding events) will increase as a result of increasing evaporation and 

5 resulting higher atmospheric water vapor that occurs with increasing temperature. (. Medium 

6 confidence) 

7 Description of evidence base 

8 The Key Finding and supporting text summarizes evidence documented in the climate science 

9 literature. 

10 Evidence for the expectation of an increase in the frequency and severity of landfalling 

1 1 atmospheric rivers on the US West Coast comes from the CMIP-based climate change projection 

12 studies of Dettinger et al. 2011; Warner et al. 2015;_Payne and Magnusdottir 2015; Gao et al. 

13 2015; Radio et al. 2015; and Hagos et al. 2016. The close connection between atmospheric rivers 

14 and water availability and flooding is based on the present-day observation studies of Guan et al. 

15 2010; Dettinger et al. 2011; Ralph et al. 2006; Neiman et al. 2011; Moore et al. 2012; and 

16 Dettinger 2013. 

17 Major uncertainties 

18 A modest uncertainty remains in the lack of a supporting detectable anthropogenic signal in the 

19 historical data to add further confidence to these projections. However, the overall increase in 

20 atmospheric rivers projected/expected is based to very large degree on the very high confidence 

2 1 there is that the atmospheric water vapor will increase. Thus, increasing water vapor coupled 

22 with little projected change in wind structure/intensity still indicates increases in the 

23 frequency/intensity of atmospheric rivers. A modest uncertainty arises in quantifying the 

24 expected change at a regional level (for example, northern Oregon vs southern Oregon) given 

25 that there are some changes expected in the position of the jet stream that might influence the 

26 degree of increase for different locations along the west coast. 

27 Assessment of confidence based on evidence and agreement, including short description of 

28 nature of evidence and level of agreement 

29 □ Very High 

30 nffigh 

31 x Medium 

32 □ Low 

33 Summary sentence or paragraph that integrates the above information 

34 Increases in atmospheric river frequency and intensity are expected along the U.S. west coast, 

35 leading to the likelihood of more frequent flooding conditions, with uncertainties remaining in 

36 the details of the spatial structure of theses along the coast (for example, northern vs southern 

37 California) 


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FIGURES 




1945 1955 1965 1975 1985 1995 2005 2015 


Year 


Figure 9.1: Poleward migration, in degrees of latitude, of the location of annual-mean tropical 
cyclone (TC) peak lifetime intensity in the western North Pacific Ocean, after accounting for the 
known regional modes of interannual (El Nino-Southern Oscillation; ENSO) and interdecadal 
(Pacific Decadal Oscillation; PDO) variability. The time series shows residuals of the 
multivariate regression of annually-averaged latitude of TC peak lifetime intensity onto the mean 
Nino-3.4 and PDO indices. Data are taken from the Joint Typhoon Warning Center (JTWC). 
Shading shows 95% confidence bounds for the trend. Annotated values at lower-right show the 
mean migration rate and its 95% confidence interval in degrees per decade for the period 1945- 
2013. (Figure source: redrawn from Kossin et al. 2016; © American Meteorological Society. 
Used with permission.). 


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Simulated Occurrence of Category 4 and 5 Tropical Cyclones 




Figure 9.2: Simulated occurrence of tropical cyclones of at least Category 4 intensity (surface 
winds of at least 59 m/s or 130 mph) for a) present-day or b) late 21st century (RCP4.5; CMIP5 
multimodel ensemble) conditions; unit: storms per decade. Simulated tropical cyclone tracks 
were obtained using the GFDL hurricane model to resimulate (at higher resolution) the tropical 
cyclone cases originally obtained from the HiRAM Cl 80 global mode. Occurrence refers to the 
number of days, over a 20-year period, in which a stonn exceeding 59 m/s intensity was centered 
within the 10° x 10° grid region, c) Difference in occurrence rate between late 21st century and 
present day [(b) minus (a)]. White regions are regions where no tropical storms occurred in the 
simulations [in (a) and (b)] or where the difference between the experiments is zero [in (c)]. 
(Figure source: redrawn from Knutson et al. 2015; © American Meteorological Society. Used 
with permission.). 


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2 

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7 


Annual Tornado Activity in the U.S. (1955-2013) 



10 

8 

6 

4 

2 

0 


1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 


CO 

<D 

O 

"O 

CD 

c 

s_ s— 

CD .O 
Q) h- 

^ O 
»- CO 

&s 

££ 

Q V 

o 

E 


Figure 9.3: Annual tornado activity in the United States over the period 1955-2013. The black 
squares indicate the number of days per year with at least one tornado rated (E)F 1 or greater, and 
the black circles and line show the decadal mean line of such tornado days. The red triangles 
indicate the number of days per year with more than 30 tornadoes rated (E)F1 or greater, and the 
red circles and line show the decadal mean of these tornado outbreaks. (Figure source: redrawn 
from Brooks et al. 2014) 


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40°W- 


Primarily Due to 


Atmospheric 


River Events 


R-CAT 1 : 200 < P< 300 
» R-CAT 2: 300 < P< 400 mm 
5 R-CAT 3: 400 < P< 500 mm 
> R-CAT 4: P > 500 mm 
120°W 


Extreme-Precipitation Events 
at US Coop Stations, 1950-2008 


90°W 


75° W 


AR Frequency and IVT 


90 N 


60 N 


30 N 


30 S 


60 S 


60°E 120°E 180° 120°W 60°W 0° 

— *-500kg/m/s 


0 10 20 30 40 50 60 70 

Figure 9.4: (upper left) Atmospheric rivers depicted in Special Sensor Microwave Imager 
(SSM/I) measurements of SSM/I total column water vapor leading to extreme precipitation 
events at landfall locations, (upper right) Annual mean frequency of atmospheric river 
occurrence (for example, 12% means about 1 every 8 days) and their integrated moisture 
transport (IVT) (Guan and Waliser 2015). (lower left) ARs are the dominant synoptic storms for 
the U.S. west coast in terms of extreme precipitation (Ralph and Dettinger 2012) and (lower 
right) supply a large fraction of the annual precipitation in the U.S. west coast states (Dettinger et 
al. 2011). [Figure source: (upper left) Ralph et al. 2011, (upper right) Guan and Waliser 2015, 
(lower left) Ralph and Dettinger 2012, (lower right), Dettinger et al. 201 1; left panels, © 
American Meteorological Society. Used with permission.] 


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1 REFERENCES 

2 Allen, J.T. and M.K. Tippett, 2015: The Characteristics of United States Hail Reports: 1955- 

3 2014. Electronic Journal of Severe Storms Meteorology. 

4 Barnes, E.A. and L.M. Polvani, 2015: CMIP5 Projections of Arctic Amplification, of the North 

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Chapter 9 


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2 Future Regional Climate Change. Climate Change 2013: The Physical Science Basis. 

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8 Colle, B.A., Z. Zhang, K.A. Lombardo, E. Chang, P. Liu, and M. Zhang, 2013: Historical 

9 Evaluation and Future Prediction of Eastern North American and Western Atlantic 

10 Extratropical Cyclones in the CMIP5 Models during the Cool Season. Journal of Climate, 

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12 Del Genio, A.D., M.S. Yao, and J. Jonas, 2007: Will moist convection be stronger in a warmer 

13 climate? Geophysical Research Letters, 34, 5. http://dx.doi.org/10.1029/2007GL030525 

14 Delworth, L.T. and E.M. Mann, 2000: Observed and simulated multidecadal variability in the 

15 Northern Hemisphere. Climate Dynamics, 16, 661-676. 

1 6 http ://dx .doi .org/ 10.1 007/s003 82000007 5 

17 Dettinger, M.D., 2013: Atmospheric Rivers as Drought Busters on the U.S. West Coast. Journal 

18 of Hydrometeorology , 14, 1721-1732. http://dx.doi.Org/10.1175/JHM-D-13-02.l 

19 Dettinger, M.D. and B.L. Ingram, 2013: The Coming Megafloods. Scientific American, 308, 64- 

20 71. http://dx.doi.org/10.1038/scientificamerican0113-64 

21 Dettinger, M.D., F.M. Ralph, T. Das, P.J. Neiman, and D.R. Cayan, 2011: Atmospheric rivers, 

22 floods and the water resources of California. Water, 3, 445-478. 

23 http://dx.doi.org/10.3390/w3020445 http://www.mdpi.eom/2073-4441/3/2/445/pdf 

24 Diffenbaugh, N.S., M. Scherer, and R.J. Trapp, 2013: Robust increases in severe thunderstorm 

25 environments in response to greenhouse forcing. Proceedings of the National Academy of 

26 Sciences, 110, 16361-16366. http://dx.doi.org/10.1073/pnas.1307758110 

27 Dunstone, N.J., D.M. Smith, B.B.B. Booth, L. Hermanson, and R. Eade, 2013: Anthropogenic 

28 aerosol forcing of Atlantic tropical storms. Nature Geoscience, 6, 534-539. 

29 http ://dx .doi .org / 1 0 . 1 03 8/ngeo 1854 

30 Eisner, J.B., S.C. Eisner, and T.H. Jagger, 2015: The increasing efficiency of tornado days in the 

31 United States. Climate Dynamics, 45, 651-659. http://dx.doi.org/10.1007/s00382-014-2277- 

32 3 

33 Emanuel, K., 2015: Effect of Upper-Ocean Evolution on Projected Trends in Tropical Cyclone 

34 Activity. Journal of Climate, 28, 8165-8170. http://dx.doi.org/10.1175/JCLI-D-15-0401.! 


329 



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Chapter 9 


1 Emanuel, K.A., 2013: Downscaling CMIP5 climate models shows increased tropical cyclone 

2 activity over the 21st century. Proceedings of the National Academy of Sciences, 1 10, 12219- 

3 12224. http://dx.doi.org/10.1073/pnas.1301293110 

4 Evan, A.T., 2012: Atlantic hurricane activity following two major volcanic eruptions. Journal of 

5 Geophysical Research, 117,D06101. http://dx.doi.org/10.1029/2011JD016716 

6 Evan, A.T., G.R. Foltz, D. Zhang, and D J. Vimont, 2011: Influence of African dust on ocean- 

7 atmosphere variability in the tropical Atlantic. Nature Geoscience, 4, 762-765. 

8 http ://dx .doi .org / 1 0 . 1 03 8/ngeo 1276 

9 Evan, A.T., D ,J. Vimont, A.K. Heidinger, J.P. Kossin, and R. Bennartz, 2009: The role of 

10 aerosols in the evolution of tropical North Atlantic Ocean temperature anomalies. Science, 

11 324,778-781. http://dx.doi.org/10.1126/science.1167404 

12 Francis, J. and N. Skific, 2015: Evidence linking rapid Arctic warming to mid-latitude weather 

13 patterns. Philosophical Transactions of the Royal Society A: Mathematical, Physical and 

14 Engineering Sciences, 373. http://dx.doi.org/10.1098/rsta.2014.0170 

15 Gao, Y., J. Lu, L.R. Leung, Q. Yang, S. Hagos, and Y. Qian, 2015: Dynamical and 

16 thermodynamical modulations on future changes of landfalling atmospheric rivers over 

17 western North America. Geophysical Research Letters, 42, 7179-7186. 

18 http://dx.doi.org/10.1002/2015GL065435 

19 Gensini, V. A. and T.L. Mote, 2014: Estimations of Hazardous Convective Weather in the United 

20 States Using Dynamical Downscaling. Journal of Climate, 27, 6581-6589. 

2 1 http ://dx .doi .org / 1 0 . 1 1 75/JCLI-D- 13-00777 . 1 

22 Gensini, V.A., C. Ramseyer, and T.L. Mote, 2014: Future convective environments using 

23 NARCCAP. International Journal of Climatology , 34, 1699-1705. 

24 http ://dx .doi .org / 1 0 . 1 002/joc .3769 

25 Guan, B., N.P. Molotch, D.E. Waliser, EJ. Fetzer, and PJ. Neiman, 2010: Extreme snowfall 

26 events linked to atmospheric rivers and surface air temperature via satellite measurements. 

27 Geophysical Research Letters, 37,n/a-n/a. http://dx.doi.org/10.1029/2010GL044696 

28 Guan, B. and D.E. Waliser, 2015: Detection of atmospheric rivers: Evaluation and application of 

29 an algorithm for global studies. Journal of Geophysical Research: Atmospheres , 120, 12514- 

30 12535. http://dx.doi.org/10.1002/2015JD024257 

31 Hagos, S.M., L.R. Leung, J.-H. Yoon, J. Lu, and Y. Gao, 2016: A projection of changes in 

32 landfalling atmospheric river frequency and extreme precipitation over western North 

33 America from the Large Ensemble CESM simulations. Geophysical Research Letters, 43, 

34 1357-1363. http://dx.doi.org/10.1002/2015GL067392 


330 



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Chapter 9 


1 Hall, T. and K. Hereid, 2015: The frequency and duration of U.S. hurricane droughts. 

2 Geophysical Research Letters , 42, 3482-3485. http://dx.doi.org/10.1002/2015GL063652 

3 Hart, R.E., D.R. Chavas, and M.P. Guishard, 2016: The Arbitrary Definition of the Current 

4 Atlantic Major Hurricane Landfall Drought. Bulletin of the American Meteorological Society, 

5 97,713-722. http://dx.doi.org/10.1175/BAMS-D-15-00185T 

6 Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Bronnimann, Y. 

7 Charabi, FJ. Dentener, EJ. Dlugokencky, D.R. Easterling, A. Kaplan, B J. Soden, P.W. 

8 Thorne, M. Wild, and P.M. Zhai, 2013: Observations: Atmosphere and Surface. Climate 

9 Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth 

10 Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. 

1 1 Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and 

12 P.M. Midgley, Eds. Cambridge University Press, Cambridge, United Kingdom and New 

13 York, NY, USA, 159-254. http://dx.doi.org/10.1017/CB09781107415324.008 

14 www .climatechange20 1 3 .org 

15 Held, I.M. and B J. Soden, 2006: Robust responses of the hydrological cycle to global warming. 

16 Journal of Climate, 19,5686-5699. http://dx.doi.org/10.1175/jcli3990T 

17 Hoogewind, K.A., M.E. Baldwin, and RJ. Trapp, 2016: Climate change and hazardous 

18 convective weather in the United States: Insights from high-resolution dynamical 

19 downscaling. Journal of Climate, Submitted. 

20 Huang, P., I.I. Lin, C. Chou, and R.-H. Huang, 2015: Change in ocean subsurface environment to 

21 suppress tropical cyclone intensification under global warming. Nature Communications , 6, 

22 7188. http://dx.doi.org/10.1038/ncomms8188 http://dx.doi.org/10.1038/ncomms8188 

23 IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group 

24 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 

25 Cambridge University Press, Cambridge, UK and New York, NY, 1535 pp. 

26 http://dx.doi.org/10.1017/CB09781 107415324 www .climatechange20 1 3 .org 

27 Klotzbach, PJ. and C.W. Landsea, 2015: Extremely Intense Hurricanes: Revisiting Webster et 

28 al. (2005) after 10 Years. Journal of Climate, 28, 7621-7629. 

29 http://dx.doi.org/10.1175/JCLI-D-15-0188T 

30 Knutson, T.R., J.J. Sirutis, M. Zhao, R.E. Tuleya, M. Bender, G.A. Vecchi, G. Villarini, and D. 

3 1 Chavas, 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty- 

32 First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of 

33 Climate, 28,7203-7224. http://dx.doi.org/10.1175/JCLI-D-15-0129T 

34 Kossin, J., 2016: Hurricane intensification along U. S. coast suppressed during active hurricane 

35 periods. Nature, doi: 10. 1038/nature20783 


331 



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Chapter 9 


1 Kossin, J.P., K.A. Emanuel, and S J. Camargo, 2016: Past and Projected Changes in Western 

2 North Pacific Tropical Cyclone Exposure. Journal of Climate, 29, 5725-5739. 

3 http ://dx .doi .org / 1 0 . 1 1 75/JCLI-D- 1 6-0076 . 1 

4 Kossin, J.P., K.A. Emanuel, and G.A. Vecchi, 2014: The poleward migration of the location of 

5 tropical cyclone maximum intensity. Nature, 509, 349-352. 

6 http ://dx .doi .org / 1 0 . 1 03 8/nature 13278 

7 Kossin, J.P., T.R. Karl, T.R. Knutson, K.A. Emanuel, K.E. Kunkel, and J ,J. O’Brien, 2015: 

8 Reply to “Comments on ‘Monitoring and Understanding Trends in Extreme Storms: State of 

9 Knowledge’”. Bulletin of the American Meteorological Society, 96, 1 177-1179. 

1 0 http ://dx .doi .org / 1 0 . 1 1 75/B AMS-D- 1 4-0026 1 . 1 

1 1 Kossin, J.P., T.L. Olander, and K.R. Knapp, 2013: Trend analysis with a new global record of 

12 tropical cyclone intensity. Journal of Climate, 26, 9960-9976. 

1 3 http ://dx .doi .org / 1 0 . 1 1 75/JCLI-D- 1 3-00262 . 1 

14 Landsea, C., J. Franklin, and J. Beven, 2015: The revised Atlantic hurricane database 

15 (HURDAT2) In: (NHC), N.H.C. (ed.). NHC, Miami, FL. 

16 Lavers, D.A., R.P. Allan, G. Villarini, B. Lloyd-Hughes, D.J. Brayshaw, and AJ. Wade, 2013: 

17 Future changes in atmospheric rivers and their implications for winter flooding in Britain. 

18 Environmental Research Letters, 8,034010. http://dx.doi.org/10.1088/1748- 

19 9326/8/3/034010 

20 Lavers, D.A., F.M. Ralph, D.E. Waliser, A. Gershunov, and M.D. Dettinger, 2015: Climate 

2 1 change intensification of horizontal water vapor transport in CMIP5 . Geophysical Research 

22 Letters, 42, 5617-5625. http://dx.doi.org/10.1002/2015GL064672 

23 Mann, M.E. and K.A. Emanuel, 2006: Atlantic hurricane trends linked to climate change. Eos, 

24 Transactions of the American Geophysical Union, 87, 233-244. 

25 http://dx.doi.org/10.1029/2006E0240001 

26 Mann, M.E., B.A. Steinman, and S.K. Miller, 2014: On forced temperature changes, internal 

27 variability, and the AMO. Geophysical Research Letters, 41 , 3211-3219. 

28 http://dx.doi.org/10.1002/2014GL059233 

29 Marinaro, A., S. Hilberg, D. Changnon, and J.R. Angel, 2015: The North Pacific-Driven Severe 

30 Midwest Winter of 2013/14. Journal of Applied Meteorology and Climatology, 54, 2141- 

31 2151. http://dx.doi.org/10.1175/JAMC-D-15-0084T 

32 Melillo, J.M., T.C. Richmond, and G.W. Yohe, eds. Climate Change Impacts in the United 

33 States: The Third National Climate Assessment. 2014, U.S. Global Change Research 

34 Program: Washington, D.C. 842. http://dx.doi.org/10.7930/J0Z31WJ2. 


332 



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Chapter 9 


1 Moore, B J., PJ. Neiman, FM. Ralph, and F.E. Barthold, 2012: Physical Processes Associated 

2 with Heavy Flooding Rainfall in Nashville, Tennessee, and Vicinity during 1-2 May 2010: 

3 The Role of an Atmospheric River and Mesoscale Convective Systems. Monthly Weather 

4 Review, 140,358-378. http://dx.doi.Org/10.1175/MWR-D-ll-00126.l 

5 Murakami, H., G.A. Vecchi, T.L. Delworth, K. Paffendorf, L. Jia, R. Gudgel, and F. Zeng, 2015: 

6 Investigating the Influence of Anthropogenic Forcing and Natural Variability on the 2014 

7 Hawaiian Hurricane Season. Bulletin of the American Meteorological Society, 96, S 1 15- 

8 Si 19. http://dx.doi.Org/10.1175/BAMS-D-15-00119.l 

9 Neiman, P J., L ,J. Schick, F.M. Ralph, M. Hughes, and G.A. Wick, 2011: Flooding in Western 

10 Washington: The Connection to Atmospheric Rivers. Journal of Hydrometeorology , 12, 

11 1337-1358. http://dx.doi.Org/10.1175/2011JHM1358.l 

12 Newell, R.E., N.E. Newell, Y. Zhu, and C. Scott, 1992: Tropospheric rivers? - A pilot study. 

13 Geophysical Research Letters, 19,2401-2404. http://dx.doi.org/10.1029/92GL02916 

14 Payne, A.E. and G. Magnusdottir, 2015: An evaluation of atmospheric rivers over the North 

15 Pacific in CMIP5 and their response to warming under RCP 8.5. Journal of Geophysical 

16 Research: Atmospheres, 120,11,173-11,190. http://dx.doi.org/10.1002/2015JD023586 

17 Perlwitz, J., M. Hoerling, and R. Dole, 2015: Arctic Tropospheric Warming: Causes and 

1 8 Linkages to Lower Latitudes . Journal of Climate, 28, 2154-2167. 

1 9 http ://dx .doi .org / 10.1 1 75/JCLI-D- 1 4-00095 . 1 

20 Radic, V., A.J. Cannon, B . Menounos, and N. Gi, 2015: Luture changes in autumn atmospheric 

21 river events in British Columbia, Canada, as projected by CMIP5 global climate models. 

22 Journal of Geophysical Research: Atmospheres , 120, 9279-9302. 

23 http ://dx .doi .org / 10.1 002/20 1 5 JD023279 

24 Ralph, L.M. and M.D. Dettinger, 2012: Historical and National Perspectives on Extreme West 

25 Coast Precipitation Associated with Atmospheric Rivers during December 2010. Bulletin of 

26 the American Meteorological Society, 93, 783-790. http://dx.doi.org/10J 175/BAMS-D-l 1- 

27 00188.1 

28 Ralph, L.M., PJ. Neiman, G.A. Wick, S.I. Gutman, M.D. Dettinger, D.R. Cayan, and A.B. 

29 White, 2006: blooding on California's Russian River: Role of atmospheric rivers. 

30 Geophysical Research Letters, 33,L13801. http://dx.doi.org/10.1029/2006GL026689 

3 1 Robinson, E.D., RJ. Trapp, and M.E. Baldwin, 2013: The Geospatial and Temporal 

32 Distributions of Severe Thunderstorms from High-Resolution Dynamical Downscaling. 

33 Journal of Applied Meteorology and Climatology , 52, 2147-2161. 

34 http://dx.doi.org/ 10 . 1 1 75/JAMC-D- 12-0131.1 


333 



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Chapter 9 


1 Rutz, J.J., W.J. Steenburgh, and F.M. Ralph, 2014: Climatological Characteristics of 

2 Atmospheric Rivers and Their Inland Penetration over the Western United States. Monthly 

3 Weather Review, 142,905-921. http://dx.doi.Org/10.1175/MWR-D-13-00168.l 

4 Schar, C., C. Frei, D. Liithi, and H.C. Davies, 1996: Surrogate climate-change scenarios for 

5 regional climate models. Geophysical Research Letters, 23, 669-672. 

6 http://dx.doi.org/10.1029/96GL00265 

7 Screen, J.A., C. Deser, and L. Sun, 2015: Projected changes in regional climate extremes arising 

8 from Arctic sea ice loss. Environmental Research Letters, 10, 084006. 

9 http://dx.doi.Org/10.1088/1748-9326/10/8/084006 

10 Seeley, J.T. and D.M. Romps, 2015: The Effect of Global Warming on Severe Thunderstorms in 

1 1 the United States. Journal of Climate, 28, 2443-2458. http://dx.doi.org/10T 175/JCLI-D-14- 

12 00382.1 

13 Smith, A.B . and R.W. Katz, 2013: U.S . billion-dollar weather and climate disasters: Data 

14 sources, trends, accuracy and biases . Natural Hazards, 67, 387-410. 

15 http://dx.doi.org/10.1007/sll069-013-0566-5 

16 Sobel, A.H., SJ. Camargo, T.M. Hall, C.-Y. Lee, M.K. Tippett, and A.A. Wing, 2016: Human 

17 influence on tropical cyclone intensity. Science, 353, 242-246. 

1 8 http ://dx .doi .org / 10.11 26/science ,aaf6574 

19 Stevens, B., 2015: Rethinking the Lower Bound on Aerosol Radiative Lorcing. Journal of 

20 Climate, 28,4794-4819. http://dx.doi.org/10.1175/JCLI-D-14-00656T 

21 Thompson, D.W.J. and S. Solomon, 2009: Understanding Recent Stratospheric Climate Change. 

22 Journal of Climate, 22, 1934-1943. http://dx.doi.org/10.1175/2008JCLI2482T 

23 Tippett, M.K., 2014: Changing volatility of U.S. annual tornado reports. Geophysical Research 

24 Letters, 41, 6956-6961. http://dx.doi.org/10.1002/2014GL061347 

25 Trapp, R.J. and K.A. Hoogewind, 2016: The Realization of Extreme Tornadic Storm Events 

26 under Luture Anthropogenic Climate Change. Journal of Climate, 29, 5251-5265. 

27 http://dx.doi.org/10T 175/JCLI-D-15-0623.1 

28 Trapp, R.J., E.D. Robinson, M.E. Baldwin, N.S. Diffenbaugh, and B.R.J. Schwedler, 2011: 

29 Regional climate of hazardous convective weather through high-resolution dynamical 

30 downscaling. Climate Dynamics, 37, 677-688. http://dx.doi.org/10.1007/s00382-010-0826-y 

31 Tuleya, R.E., M. Bender, T.R. Knutson, J.J. Sirutis, B. Thomas, and I. Ginis, 2016: Impact of 

32 Upper-Tropospheric Temperature Anomalies and Vertical Wind Shear on Tropical Cyclone 


334 



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Chapter 9 


1 Evolution Using an Idealized Version of the Operational GFDL Hurricane Model. Journal of 

2 the Atmospheric Sciences, 73, 3803-3820. http://dx.doi.Org/10.1175/JAS-D-16-0045.l 

3 Tung, K.-K. and J. Zhou, 2013: Using data to attribute episodes of warming and cooling in 

4 instrumental records. Proceedings of the National Academy of Sciences. 

5 http ://dx .doi .org/ 1 0 . 1 07 3/pnas .121 247 1110 

6 Van Klooster, S.L. and P.J. Roebber, 2009: Surface-Based Convective Potential in the 

7 Contiguous United States in a Business-as-Usual Future Climate. Journal of Climate, 22, 

8 3317-3330. http://dx.doi.Org/10.1175/2009JCFI2697.l 

9 Vose, R.S., S. Applequist, M.A. Bourassa, S.C. Pryor, RJ. Barthelmie, B. Blanton, P.D. 

10 Bromirski, H.E. Brooks, A.T. DeGaetano, R.M. Dole, D.R. Easterling, R.E. Jensen, T.R. 

1 1 Karl, R.W. Katz, K. Klink, M.C. Kruk, K.E. Kunkel, M.C. MacCracken, T.C. Peterson, K. 

12 Shein, B.R. Thomas, J.E. Walsh, X.F. Wang, M.F. Wehner, D.J. Wuebbles, and R.S. Young, 

13 2014: Monitoring and understanding changes in extremes: Extratropical storms, winds, and 

14 waves. Bulletin of the American Meteorological Society, 95, 377-386. 

1 5 http ://dx .doi .org / 1 0 . 1 1 75/B AMS-D- 1 2-00 1 62 . 1 

16 Walsh, K.J.E., SJ. Camargo, G.A. Vecchi, A.S. Daloz, J. Eisner, K. Emanuel, M. Horn, Y.-K. 

17 Lim, M. Roberts, C. Patricola, E. Scoccimarro, A.H. Sobel, S. Strazzo, G. Villarini, M. 

18 Wehner, M. Zhao, J.P. Kossin, T. LaRow, K. Oouchi, S. Schubert, H. Wang, J. Bacmeister, 

19 P. Chang, F. Chauvin, C. Jablonowski, A. Kumar, H. Murakami, T. Ose, K.A. Reed, R. 

20 Saravanan, Y. Yamada, C.M. Zarzycki, P.L. Vidale, J.A. Jonas, and N. Henderson, 2015: 

21 Hurricanes and Climate: The U.S. CLIVAR Working Group on Hurricanes. Bulletin of the 

22 American Meteorological Society, 96, 997-1017. http://dx.doi.org/10.1175/BAMS-D-13- 

23 00242.1 

24 Walsh, K.J.E., J.L. McBride, PJ. Klotzbach, S. Balachandran, SJ. Camargo, G. Holland, T.R. 

25 Knutson, J.P. Kossin, T.-c. Lee, A. Sobel, and M. Sugi, 2016: Tropical cyclones and climate 

26 change. Wiley Interdisciplinary Reviews: Climate Change, 7, 65-89. 

27 http ://dx .doi .org / 10.1 002/wcc .371 

28 Wang, X.L., Y. Feng, G.P. Compo, V.R. Swail, F.W. Zwiers, R.J. Allan, and P.D. Sardeshmukh, 

29 2012: Trends and low frequency variability of extra-tropical cyclone activity in the ensemble 

30 of twentieth century reanalysis. Climate Dynamics, 40, 2775-2800. 

3 1 http ://dx .doi .org / 10.1 007/s003 82-0 12-1450-9 

32 Wang, X.L., V.R. Swail, and F.W. Zwiers, 2006: Climatology and changes of extratropical 

33 cyclone activity: Comparison of ERA-40 with NCEP-NCAR reanalysis for 1958-2001 . 

34 Journal of Climate, 19,3145-3166. http://dx.doi.Org/10.1175/JCLI3781.l 


335 



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Chapter 9 


1 Warner, M.D., C.F. Mass, and E.P.S. Jr., 2015: Changes in Winter Atmospheric Rivers along the 

2 North American West Coast in CMIP5 Climate Models. Journal of Hydrometeorology , 16, 

3 118-128. http://dx.doi.Org/10.1175/JHM-D-14-0080.l 

4 Wehner, M., Prabhat, K.A. Reed, D. Stone, W.D. Collins, and J. Bacmeister, 2015: Resolution 

5 Dependence of Future Tropical Cyclone Projections of CAM5 .1 in the U.S . CLIVAR 

6 Hurricane Working Group Idealized Configurations. Journal of Climate, 28, 3905-3925. 

7 http ://dx .doi .org / 1 0 . 1 1 75/JCLI-D- 1 4-003 1 1 . 1 

8 Yang, X., G.A. Vecchi, T.L. Delworth, K. Paffendorf, L. Jia, R. Gudgel, F. Zeng, and S.D. 

9 Underwood, 2015: Extreme North America Winter Storm Season of 2013/14: Roles of 

10 Radiative Forcing and the Global Warming Hiatus. Bulletin of the American Meteorological 

11 Society, 96,S25-S28. http://dx.doi.Org/10.1175/BAMS-D-15-00133.l 

12 Zhang, R., T.F. Delworth, R. Sutton, D.F.R. Hodson, K.W. Dixon, I.M. Held, Y. Kushnir, J. 

13 Marshall, Y. Ming, R. Msadek, J. Robson, AJ. Rosati, M. Ting, and G.A. Vecchi, 2013: 

14 Have aerosols caused the observed Atlantic multidecadal variability? Journal of the 

15 Atmospheric Sciences, 70, 1135-1144. http://dx.doi.org/10.1175/jas-d-12-033Ll 

16 Zhu, Y. and R.E. Newell, 1998: A Proposed Algorithm for Moisture Fluxes from Atmospheric 

17 Rivers. Monthly Weather Review, 126,725-735. http://dx.doi.org/10.1175/1520- 

1 8 0493( 1998)1 26<0725 : APAFMF>2 .0 ,CO;2 


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Chapter 10 


1 10. Changes in Land Cover and Terrestrial Biogeochemistry 

2 KEY FINDINGS 

3 1 . Changes in land use and land cover due to human activities produce changes in surface 

4 albedo and in atmospheric aerosol and greenhouse gas concentrations. These combined 

5 effects have recently been estimated to account for 40% ± 16% of the human-caused global 

6 radiative forcing from 1850 to 2010 (high confidence). As a whole, the terrestrial biosphere 

7 (soil, plants) is a net “sink” for carbon (drawing down carbon from the atmosphere) and this 

8 sink has steadily increased since 1980, in part due to CO 2 fertilization ( very high confidence). 

9 The future strength of the land sink is uncertain and dependent on ecosystem feedbacks; the 

10 possibility of the land becoming a net carbon source cannot be excluded (very high 

1 1 confidence). 

12 2. The increased occurrence and severity of drought has led to large changes in plant 

13 community structure with subsequent effects on carbon distribution and cycling within 

14 ecosystems (for example, forests, grasslands). Uncertainties about future land use changes 

1 5 (for example, policy or mitigation measures) and about how climate change will affect land 

16 cover change make it difficult to project the magnitude and sign of future climate feedbacks 

17 from land cover changes. (High confidence) 

18 3. Since 1901, the consecutive number of both frost-free days and the length of the 

19 corresponding growing season has increased for all regions of the United States. However, 

20 there is important variability at smaller scales, with some locations showing decreases of as 

21 much as one to two weeks. Plant productivity has not increased linearly with the increased 

22 number of frost-free days or with the longer growing season due to temperature thresholds 

23 and requirements for growth as well as seasonal limitations in water and nutrient availability 

24 (very high confidence). Future consequences of changes to the growing season for plant 

25 productivity are uncertain. 

26 4. Surface temperatures are often higher in urban areas than in surrounding rural areas, for a 

27 number of reasons including the concentrated release of heat from buildings, vehicles, and 

28 industry. In the United States, this urban heat island (UHI) effect results in daytime 

29 temperatures 0.9°-7.2°F (0.5°-4.0°C) higher and nighttime temperatures 1 .8°- 4.5°F (1 .0°- 

30 2.5°C) higher in urban areas, with larger temperature differences in humid regions (primarily 

3 1 the eastern United States) and in cities with larger populations. The UHI effect will 

32 strengthen in the future as the spatial extent and population of urban areas grow. (High 

33 confidence) 

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Chapter 10 


10.1 Introduction 


Direct changes in land use by humans are contributing to radiative forcing by altering land cover 
and therefore albedo, contributing to climate change (Chapter 2: Physical Drivers of Climate 
Change). This forcing is spatially variable in both magnitude and sign; globally averaged it is 
negative (climate cooling; Figure 2.3). Climate changes, in turn, are altering the biogeochemistry 
of land ecosystems through extended growing seasons, increased numbers of frost- free days, 
altered productivity in agricultural and forested systems, longer fire seasons, and urban-induced 
thunderstorms (Galloway et al. 2014). These changes in land use and land cover interact with 
local, regional, and global climate processes (Brown et al. 2014). The resulting changes alter 
Earth’s albedo, the carbon cycle, and atmospheric aerosols, constituting a mix of positive and 
negative feedbacks to climate change (Ward et al. 2014; Figure 10.1 and Chapter 2, Section 
2.6.2). Thus, changes to terrestrial ecosystems are a direct driver of climate change and they are 
altered by climate change in ways that affect both ecosystem productivity and, through 
feedbacks, the climate itself. 

The concept that longer growing seasons are increasing productivity in some agriculture and 
forested ecosystems was discussed in the Third National Climate Assessment (NCA3; Melillo et 
al. 2014). However, there are other consequences to a lengthened growing season that can offset 
these gains in productivity. Here we discuss these emerging complexities (Section 10.3.1) as well 
as discussing other aspects of how climate change is altering and interacting with terrestrial 
ecosystems. 


[INSERT FIGURE 10.1 HERE: 



Figure 10.1. A graphical representation of climate interactions with land use and land cover. 
(Figure source: Ward et al. 2014)] 

10.2 Terrestrial Ecosystem Interactions with the Climate System 

This report discusses changes in temperature (Ch. 6: Temperature Change), precipitation (Ch. 7: 
Precipitation Change), hydrology (Ch. 8: Droughts, Floods, and Hydrology) and extreme events 
(Ch. 9: Extreme Storms). The intersections of these topics affect the phenology, or onset of 
growth through senescence; of land cover and biogeochemistry through biophysical land surface 
properties such as albedo, energy, and hydrologic processes; and biogeochemical cycles such as 
carbon, nitrogen, and water (Ward et al. 2014; Figure 10.2). Satellite observations and ecosystem 
models suggests that biogeochemical interactions of carbon dioxide (CO 2 ) fertilization and 
nitrogen (N) deposition and land cover change are responsible for global greening (25%-50%) 
and 4% of the Earth browning between 1982 and 2009 (Zhu et al. 2016; Mao et al. 2016). A 
recent analysis shows large-scale greening in the Arctic and boreal regions of North America and 
browning in the boreal forests of eastern Alaska for the period 1984-2012 (Ju and Masek 2016). 
While several studies have documented significant green-up periods, the lengthening of the 
growing season (Chapter 10.3.1) also alters the timing of green-up (onset of growth) and brown- 


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down (senescence); where ecosystems become depleted of water resources as a result of this, the 
actual period of productive growth can be truncated (Adams et al. 2015). This section discusses 
how changes in temperature, the hydrologic cycle, extreme events, and biogeochemistry interact 
with land cover change. 

[INSERT FIGURE 10.2 HERE: 

Figure 10.2. Radiative forcings for land use/land cover change and other anthropogenic impacts 
estimated for the year 2010 referenced to the year 1850. Total anthropogenic radiative forcings 
(Myhre et al. 2013) are shown for comparison (yellow). Error lines represent uncertainties in 
total anthropogenic RF for the IPCC bars and uncertainties in land use/land cover change 
radiative forcings. The “SUM” bars show the total radiative forcings when all forcing agents are 
combined. (Figure source: Ward et al. 2014)] 

10.2.1 Temperature Change 

Interactions between temperature changes, land cover, and biogeochemistry are more complex 
than commonly assumed. Previous research suggested a fairly direct relationship between 
increasing temperatures, longer growing seasons (see Section 10.3.1), increasing plant 
productivity (e.g. Walsh et al. 2014a), and therefore also an increase in CO 2 uptake. Without 
water or nutrient limitations, increased CO 2 concentrations and warm temperatures have been 
shown to extend the growing season, which may contribute to longer periods of plant activity 
and carbon uptake, but do not affect reproduction rates (Reyes-Fox et. al. 2014). However, there 
are other processes that offset benefits of a longer growing season, such as changes in water 
availability and demand for water (e.g., Georgakakos et al. 2014; Hibbard et al. 2014). For 
instance, increased dry conditions can lead to wildfire (e.g., Hatfield et al. 2014; Joyce et al. 
2014) and urban temperatures can contribute to urban-induced thunderstorms in the southeastern 
United States (Ashley et al. 2012). Temperature benefits of early onset of plant development in a 
longer growing season can be offset by (1) freeze damage caused by late-season frosts; (2) limits 
to growth because of shortening of the photoperiod later in the season; or (3) by shorter chilling 
periods required for leaf unfolding by many plants (Fu et al. 2013; Gu et al. 2008). In the case of 
the 2012 draught, a warm spring reduced the carbon cycle impact of the drought by inducing 
earlier carbon uptake (Wolf et al. 2016). New evidence points to longer temperature-driven 
growing seasons for grasslands that may facilitate earlier onset of growth, but also that 
senescence is typically earlier (Fridley et al. 2016). In addition to changing CO 2 uptake, higher 
temperatures can also enhance soil decomposition rates, thereby adding more CO 2 to the 
atmosphere. Similarly temperature, as well as changes in the seasonality and intensity of 
precipitation, can influence nutrient and water availability (leading to both shortages and 
excesses) thereby influencing rates and magnitudes of decomposition (Galloway et al. 2014). 


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1 10.2.2 Water Cycle Changes 

2 The global hydrological cycle is expected to intensify under climate change as a consequence of 

3 increased temperatures in the troposphere. The consequences of the increased water-holding 

4 capacity of a wanner atmosphere are longer and more frequent droughts and less frequent but 

5 more severe precipitation events and cyclonic activity (see Ch. 9: Extreme Storms for an in- 

6 depth discussion of extreme storms). More intense rain events and storms can lead to flooding 

7 and ecosystem disturbances, thereby altering ecosystem function and carbon cycle dynamics. For 

8 an extensive review of precipitation changes and droughts, floods, and hydrology, see Chapters 7 

9 and 8 in this report. 

10 From the perspective of the land biosphere, drought has strong effects on ecosystem productivity 

1 1 and carbon storage by reducing photosynthesis and increasing the risk of wildfire, pest 

12 infestation, and disease susceptibility. Thus, droughts of the future will affect carbon uptake and 

13 storage, leading to feedbacks to the climate system (See Chapter 1 1 for Arctic/climate/wildfire 

14 feedbacks; also see Schlesinger et al. 2016). Reduced productivity as a result of extreme drought 

15 events can also extend for several years post-drought (i.e., drought legacy effects; Frank et al. 

16 2016; Reichstein et al. 2013; Anderegg et al. 2015). The area of ecosystems under active drought 

17 and drought recovery is increasing (Schwalm et al. in review Nature), and recent work suggests 

1 8 that as drought events become more severe and frequent, the period between drought events may 

19 become shorter than ecosystem drought recovery time, leading to widespread degradation of land 

20 carbon si nk s (Schwalm et al. in review Nature). In 201 1, the most severe drought on record in 

21 Texas led to statewide regional tree mortality of 6.2%, or nearly nine times greater than the 

22 average annual mortality in this region (approximately 0.7%) (Moore et al. 2016). The net effect 

23 on carbon storage was estimated to be a redistribution of 24-30 Tg C from the live to dead tree 

24 carbon pool, which is equal to 6%-7% of pre-drought live tree carbon storage in Texas state 

25 forestlands (Moore et al. 2016). Another way to think about this redistribution is that the single 

26 Texas drought event equals approximately 36% of annual global carbon losses due to 

27 deforestation and land use change (Cias et al. 2013). The projected increases in temperatures and 

28 in the magnitude and frequency of heavy precipitation events, changes to snowpack, and changes 

29 in the subsequent water availability for agriculture and forestry may lead to similar rates of 

30 mortality or changes in land cover. Increasing frequency and intensity of drought across northern 

3 1 ecosystems reduces total observed organic matter export, leads to oxidized wetland soils, and 

32 releases stored contaminants into streams after rain events (Szkokan-Emilson et al. 2016). The 

33 consequences of drought and changes in the growing season have also increased demand for 

34 irrigation water in all major agricultural areas of the United States from 2000 to 2008, resulting 

35 in unsustainable use of groundwater resources (Marston et al. 2015). 

36 10.2.3 Biogeochemistry 

37 Terrestrial biogeochemical cycles play a key role in Earth’s climate system, including by 

38 affecting land-atmosphere fluxes of many aerosol precursors and greenhouse gases, including 


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1 carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 0). As such, changes in the 

2 terrestrial ecosphere can drive climate change. At the same time, biogeochemical cycles are 

3 sensitive to changes in climate and atmospheric composition. 

4 Historically, increased atmospheric C0 2 concentrations have led to increased plant production 

5 (known as C0 2 fertilization) and longer-term storage of carbon in biomass and soils (SOCCR-2 

6 Chapter 12). Whether increased atmospheric C0 2 will continue to lead to long-term storage of 

7 carbon in terrestrial ecosystems depends on whether C0 2 fertilization simply intensifies the rate 

8 of short-term carbon cycling (for example, by stimulating respiration, root exudation, and high 

9 turnover root growth) or whether the additional carbon is used by plants to build more wood or 

10 tissues that, once senesced, decompose into long-lived soil organic matter (SOCCR-2 Chapter 

11 19). Under increased C0 2 concentrations plants have been observed to optimize water use due to 

12 reduced stomatal conductance, thereby increasing water use efficiency (Keenan et al. 2013; 

13 SOCCR-2 Chapter 17). This change in water use efficiency can affect plants’ tolerance to stress 

14 and specifically to drought (SOCCR-2 Chapter 17). Due to the complex interactions of the 

15 processes that govern terrestrial biogeochemical cycling, terrestrial ecosystem responses to C0 2 

16 remains one of the largest uncertainties in predicting future climate change (Chapter 2: Physical 

17 Drivers of Climate Change). 

18 Nitrogen is a principal nutrient for plant growth and can limit or stimulate plant productivity (and 

19 carbon uptake), depending on availability. As a result, increased nitrogen deposition and natural 

20 nitrogen-cycle responses to climate change will influence the global carbon cycle. For example, 

2 1 nitrogen limitation can limit the C0 2 fertilization response of plants to elevated atmospheric C0 2 

22 (e.g., Norby et al. 2010; Zaehle et al. 2010; SOCCR-2 Chapter 17). Conversely, increased 

23 decomposition of soil organic matter in response to climate warming increases nitrogen 

24 mineralization. This shift of nitrogen from soil to vegetation can increase ecosystem carbon 

25 storage (Melillo et al. 2011; Cias et al. 2013). While the effects of increased nitrogen deposition 

26 may counteract some nitrogen limitation on C0 2 fertilization, the importance of nitrogen in 

27 future carbon-climate interactions is not clear. Nitrogen dynamics are being integrated into the 

28 simulation of land carbon cycle modeling, but only two of the models in CMIP5 included 

29 coupled carbon-nitrogen interactions (Knutti and Sedlacek 2013). 

30 Many factors, including climate, atmospheric C0 2 concentrations, and nitrogen deposition rates 

3 1 influence the structure of the plant community and therefore the amount and biochemical quality 

32 of inputs into soils (Jandl et al. 2007; McLauchlan 2006; Smith et al. 2007; SOCCR-2 Chapter 

33 12). For example, though C0 2 losses from soils may decrease with greater nitrogen deposition, 

34 increased emissions of other greenhouse gases, such as methane (CH 4 ) and nitrous oxide (N 2 0), 

35 can offset the reduction in C0 2 (Liu and Greaver 2009; SOCCR-2 Chapter 12). The dynamics of 

36 soil organic carbon under the influences of climate change are poorly understood and therefore 

37 not well represented in models. As a result, there is high uncertainty in soil carbon stocks in 

38 model simulations (Todd-Brown et al. 2013; Tian et al. 2015; SOCCR-2 Chapter 12). 


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Future emissions of many aerosol precursors are expected to be affected by a number of climate- 
related factors, in part because of changes in aerosol and aerosol precursors from the terrestrial 
biosphere. For example, volatile organic compounds (VOCs) are a significant source of 
secondary organic aerosols, and biogenic sources of VOCs exceed emissions from the industrial 
and transportation sectors (Guenther et al. 2006). Isoprene is one of the most important biogenic 
VOCs, and isoprene emissions are strongly dependent on temperature and light, as well as other 
factors like plant type and leaf age (Guenther et al. 2006). Higher temperatures are expected to 
lead to an increase in biogenic VOC emissions. Atmospheric CO 2 concentration can also affect 
isoprene emissions (e.g., Rosenstiel et al. 2003). Changes in biogenic VOC emissions can impact 
aerosol formation and feedbacks with climate (Chapter 2: Physical Drivers of Climate Change). 
Increased biogenic VOC emissions can also impact ozone and the atmospheric oxidizing 
capacity (Pyle et al. 2007). Conversely, increases in nitrogen oxide (NO x ) pollution produce 
tropospheric ozone (O3), which has damaging effects on vegetation. For example, a recent study 
estimated yield losses for maize and soybean production of up to 5% to 10% due to increases in 
O3 (McGrath et al. 2015). 

While the climate influences land cover and biogeochemistry, the converse is also true (Kalnay 
and Cai 2003). Changes in land cover and land use, including deforestation, afforestation, land 
cultivation, and development together impact surface albedo, CO 2 , CH 4 , O3, and aerosols. With 
all forcing agents considered together, one study estimated that 0.9 W/m 2 , or 40% of the present- 
day human-caused radiative forcing can be attributed to land use and land cover change (Ward et 
al. 2014). Continued land-use change is expected to contribute between 0.9 and 1.9 W/m 2 to 
direct radiative forcing by 2100 (Ward et al. 2014). The net radiative forcing due specifically to 
fire, after accounting for short-lived forcing agents O3 and aerosols, in addition to long-lived 
greenhouse gases and land albedo change, both now and in the future, is estimated to be near- 
zero due to regrowth of forests following fires offsetting the release of CO 2 in the fire (Ward and 
Mahowald 2015). 

10.2.4 Extreme Events and Disturbance 

This section builds on the physical overview provided in earlier chapters to frame how the 
intersects of climate, extreme events, and disturbance affect regional land cover and 
biogeochemistry. In addition to overall trends in temperature (Ch. 7) and precipitation (Ch. 8), 
changes in modes of variability such as the Pacific Decadal Oscillation (PDO) and the El Nino- 
Southern Oscillation (ENSO) (Ch. 5: Circulation and Variability) can contribute to drought 
cycles in the United States, which leads to unanticipated changes in disturbance regimes in the 
terrestrial biosphere (e.g., Kam et al., 2014). Drought may exacerbate the rate of plant invasions 
by non-native species in rangelands and grasslands (Moore et al. 2016). Land cover changes such 
as encroachment and invasion of non-native species can in turn lead to increased frequency of 
disturbance such as fires. Disturbance events alter soil moisture, which in addition to being 
affected by evapotranspiration and precipitation (Ch. 8: Droughts, Flood and Hydrology), is 
controlled by canopy and rooting architecture as well as soil physics. Invasive plants may be 


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1 directly responsible for changes in fire regimes through increased biomass, changes in the 

2 distribution of flammable biomass, increased flammability, and altered timing of fuel drying, 

3 while others may be “fire followers” whose abundances increase as a result of shortening the 

4 fire return interval (e.g., Lambert et al. 2010). Changes in land cover resulting from fire and 

5 alteration of disturbance regimes affects long-term carbon exchange between the atmosphere and 

6 biosphere (e.g., Moore et al. 2016). Recent extensive diebacks and changes in plant cover due to 

7 drought have interacted with regional carbon cycle dynamics including carbon release from 

8 biomass and reductions in carbon uptake from the atmosphere, though plant re-growth may 

9 offset emissions (Vose et al. 2016). The current meteorological drought in California (described 

10 in Ch. 8: Droughts, Floods and Hydrology), combined with wanning, will result in long-term 

1 1 changes in land cover, leading to increased probability of drought and wildfire and in ecosystem 

12 shifts (Diffenbaugh et al. 2015). California’s recent drought has also resulted in measureable 

13 canopy water losses, posing long-tenn hazards to forest health and biophysical feedbacks to 

14 regional climate (Anderegg et al. 2015; Asner et al. 2016; Mann and Gleick 2015). Multi-year, or 

15 severe meteorological and hydrologic (see Ch. 8: Droughts, Floods, and Hydrology for 

16 definitions) droughts can also affect stream biogeochemistry and riparian ecosystems by 

17 concentrating sediments and nutrients (Vose et al. 2016). 

18 Changes in the variability of hurricanes and winter storm events (Ch. 9: Extreme Storms) also 

19 affect the terrestrial biosphere, as shown in studies comparing historic and future (projected) 

20 extreme events in the western United States and how these translate into changes in the regional 

21 water balance, fire, and streamflow. Composited across 10 global climate models (GCMs) 

22 summer (June-August) water-balance deficit in the future (2030-2059) increases compared to 

23 that under historical (1916-2006) conditions. Portions of the Southwest that have significant 

24 monsoon precipitation, and some mountainous areas of the Pacific Northwest are exempt from 

25 this deficit (Littell et al. 2016). Projections for 2030-2059 suggest that the Columbia Basin, 

26 upper Snake River, southeastern California, and southwestern Oregon may exceed extreme low 

27 flows less frequently than they did historically (1916-2006). Given the historical relationships 

28 between fire occurrence and drought indicators such as water-balance deficit and streamflow, 

29 climate change can be expected to have significant effects on fire occurrence and area burned 

30 (Littell et al. 2016, 201 1; Eisner et al. 2010). 

3 1 Climate change in the northern high latitudes is directly contributing to increased fire occurrence 

32 (Ch. 1 1 : Arctic); in the coterminous United States, climate-induced changes in fires, changes in 

33 direct human ignitions, and land management practices all significantly contribute to wildfire 

34 trends. Wildfires in the western United States are often ignited by lightning, but management 

35 practices such as fire suppression contribute to fuels and amplify the intensity and spread of 

36 wildfire. Unintentional ignition by campfires or intentional human ignitions are also 

37 compounded by increasingly dry and vulnerable fuels, which build up with fire suppression or 

38 human settlements. 


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1 10.3 Climate Indicators and Agricultural and Forest Responses 

2 Agricultural production has changed the surface of the Earth and influenced hydrology as well as 

3 the flux and distribution of carbon. Recent studies indicate a correlation between the expansion 

4 of agriculture and the global amplitude of the CO 2 uptake and emissions (Zeng et al. 2014; Gray 

5 et al. 2014). Conversely, agricultural production is increasingly disrupted by climate and extreme 

6 weather events, and these impacts are expected to be augmented by mid-century and beyond for 

7 most crops (Lobell and Tebaldi 2014; Challinor et al. 2014) and livestock. Precipitation extremes 

8 put pressure on agricultural soil and water assets and lead to increased irrigation, shrinking 

9 aquifers, and ground subsidence. While human adaptation of crop management has mitigated 

10 many near-tenn consequences, climate change effects on agriculture will have long-term impacts 

11 on food security (Brown et al. 2015). Global Climate Models (GCMs) differ with regard to how 

12 increasing atmospheric CO 2 concentrations affect plant productivity through CO 2 fertilization or 

13 downregulation as well as the strength of carbon cycle feedbacks (Anav et al. 2013; Chapter 2: 

14 Physical Drivers of Climate Change). When CO 2 effects on photosynthesis and transpiration are 

15 removed from Global Gridded Crop Models, simulated response to climate across the models is 

16 comparable, suggesting that model parameterizations representing these process remains 

17 uncertain (Rosenzweig et al. 2014). 

18 10.3.1 Changes in the Frost-Free and Growing Seasons 

19 The growing season is the part of the year in which temperatures are favorable to plant growth. A 

20 basic metric by which this is measured is the frost-free season. The U. S. Department of 

21 Agriculture Natural Resources Conservation Service defines the frost-free period using a range 

22 of thresholds. They calculate the average date of the last day with temperature below 24°F, 28°F, 

23 and 32°F in the spring and the average date of the first day with temperature below 24°F, 28°F, 

24 and 32°F in the fall, at various probabilities. They then define the frost-free season at three index 

25 temperatures (32°F, 28°F, and 24°F), also with a range of probabilities. Fixed temperature 

26 thresholds (for example, temperature below 32°F) are often used when discussing growing 

27 season; however, different plant cover-types (for example, forest, agricultural, shrub, tundra) 

28 have different temperature thresholds for growth, and different requirements/thresholds for 

29 chilling (Zhang et al. 2011; Hatfield et al. 2014). For the purposes of this report, we use the 

30 metric with a 32°F threshold to define the change in the number of “frost- free” days, and a 

31 temperature threshold of 41°F as a first-order measure of how the growing season length has 

32 changed over the observational record (Zhang et al. 2011). 

33 The NCA3 reported an increase in the growing season by as much as several weeks as a result of 

34 higher temperatures occurring earlier and later in the year (e.g., Walsh et al. 2014b; Hatfield et 

35 al. 2014; Joyce et al. 2014). NCA3 used a threshold of 32°F — that is to say, the frost-free 

36 season — to define the growing season. An update to this finding is presented in Figures 10.3 and 

37 10.4, which show changes in the frost- free and growing season, respectively, as defined above. 

38 Overall, the length of the frost-free season has increased in the contiguous United States during 


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1 the past century (Figure 10.3). The growing season changes are more variable: Growing season 

2 length increased until the late 1930s, declined slightly until the early 1970s, increased again until 

3 about 1990, and remained quasi-stable thereafter (Figure 10.4). This contrasts somewhat with 

4 changes in the length of the frost-free season presented in NCA3, which showed a continuing 

5 increase after 1980. This discrepancy is attributable to the temperature thresholds used in each 

6 indicator to define the start and end of a season. Specifically, the growing season length (41°F 

7 threshold) is more conservative than the 32°F frost-free threshold because the latter captures 

8 more days in winter, which has larger temperature trends. 

9 The lengthening of the growing season has been somewhat greater in the northern and western 

10 United States, which had increases of 1-2 weeks in many locations. In contrast, some areas in 

1 1 the Midwest, Southern Great Plains, and the Southeast had decreases of a week or more between 

12 the periods 1986-2015 and 1901-1960. These differences reflect the more general pattern of 

13 wanning and cooling nationwide (Ch. 6: Temperature Changes). Observations and models have 

14 verified that the growing season has generally increased plant productivity over most of the 

15 United States (Mao et al. 2016). 

16 Consistent with increases in growing season length and the coldest temperature of the year, plant 

17 hardiness zones have shifted northward in many areas (Daly et al. 2012). The widespread 

1 8 increase in temperature has also impacted the distribution of other climate zones in parts of the 

19 United States. For instance, there have been moderate changes in the range of the temperate and 

20 continental climate zones of the eastern United States since 1950 (Chan and Wu 2015) as well as 

21 changes in the coverage of some extreme climate zones in the western United States. In 

22 particular, the spatial extent of the “alpine tundra” zone has decreased in high-elevation areas 

23 (Diaz and Eischeid 2007) while the extent of the “hot arid” zone has increased in the Southwest 

24 (Grundstein 2008). 

25 The period over which plants are actually productive, that is, their true growing season, is a 

26 function of multiple climate factors including air temperature, number of frost days, and rainfall, 

27 as well as biophysical factors including soil physics, daylight hours, and the biogeochemistry of 

28 ecosystems (EPA 2016). Further, while growing season length is generally referred to in the 

29 context of agricultural productivity, the factors that govern which plant types will grow in a 

30 given location are common to all plants whether they are in agricultural, natural, or managed 

3 1 landscapes. Changes in both the length and the seasonality of the growing season, in concert with 

32 local environmental conditions, can have multiple effects on agricultural productivity and land 

33 cover more generally. 

34 In the context of agriculture, a longer growing season could allow for the diversification of 

35 cropping systems or allow multiple harvests within a growing season. For example, shifts in 

36 cold-hardy zones across the contiguous United States suggest widespread expansion of thermally 

37 suitable areas for the cultivation of cold-intolerant perennial agriculture (Parker and Abatzoglou 

38 2016). However, changes in available water, conversion from dry to irrigated farming, and 


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changes in sensible and latent heat exchange associated with these shifts need to be considered. 
Increasingly dry conditions under a longer growing season can alter terrestrial organic matter 
export and catalyze oxidation of wetland soils, releasing stored contaminants (for example, 
copper and nickel) into streamflow after rainfall (Szkokan-Emilson et al. 2016). Similarly, a 
longer growing season, particularly in years where water is limited, is not due to warming alone, 
but is exacerbated by higher atmospheric CO 2 concentrations that extend the active period of 
growth by plants (Reyes-Fox et al. 2014). Longer growing seasons can also limit the types of 
crops that can be grown, encourage invasive species or weed growth, or increase demand for 
irrigation, possibly beyond the limits of water availability. They could also disrupt the function 
and structure of a region’s ecosystems and could, for example, alter the range and types of 
animal species in the area. 

A longer and seasonally-shifted growing season also affects the role of terrestrial ecosystems in 
the carbon cycle. Neither seasonality of growing season (spring and summer) nor carbon, water, 
and energy fluxes should be interpreted separately when analyzing the impacts of climate 
extremes such as drought (Ch. 8: Droughts, Floods, and Hydrology; Sippel et al. 2016; Wolf et 
al. 2016). Observations and data-driven model studies suggest that losses in net terrestrial carbon 
uptake during record warm springs followed by severely hot and dry summers can be largely 
offset by carbon gains in record-exceeding warmth and early arrival of spring (Wolf et al. 2016). 
Depending on soil physics and land cover, a cool spring, however, can deplete soil water 
resources less rapidly, making the subsequent impacts of precipitation deficits less severe (Sippel 
et al. 2016). Depletion of soil moisture through early plant activity in a warm spring can 
potentially amplify summer heating, a typical lagged direct effect of an extremely warm spring 
(Frank et al. 2015). Ecosystem responses to the phenological changes of timing and extent of 
growing season and subsequent biophysical feedbacks are therefore strongly dependent on the 
timing of climate extremes (Ch. 8: Droughts, Floods, and Hydrology; Ch. 9: Extreme Storms; 
Sippel et al. 2016). 

The global Coupled Model Intercomparison Project Phase 5 (CMIP5) analyses did not explicitly 
explore future changes to the growing season. Many projected changes in North American 
climate are generally consistent across CMIP5 models, but there is substantial inter-model 
disagreement in projections of some metrics important to biophysical systems’ productivity, 
including the sign of regional precipitation changes and extreme heat events across the northern 
United States (Maloney et al. 2014). 

[INSERT FIGURE 10.3 HERE: 

Figure 10.3. Change in number of days since 1901 between the last spring occurrence of 32°F 

and first fall occurrence of 32°F for NCA4 regions of the United States. This change is expressed 

as the difference between the average number of frost- free days in 1986-2015 minus that in 

1901-1960. (Figure source: updated from Walsh et al. 2010)] 


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[INSERT FIGURE 10.4 HERE: 

Figure 10.4. The length of the growing season in the contiguous 48 states compared with a long- 
term average (1895-2015), where “growing season” is defined by a temperature threshold of 
41°F. For each year, the line represents the number of days shorter or longer than average. The 

line was smoothed using an 1 1-year moving average. Choosing a different long-term average for 
comparison would not change the shape of the data over time. (Figure source: Kunlcel et al. 

2016, EPA 2016)] 

10.3.2 Water Availability and Drought 

Drought is generally parameterized in most agricultural models as limited water availability, and 
is an integrated response of both meteorological and agricultural drought, as described in Chapter 
8 (Droughts, Floods and Hydrology). However, physiological as well as biophysical processes 
that influence land cover and biogeochemistry interact with drought through stomatal closure 
induced by elevated atmospheric CO 2 levels. This has direct impacts on plant transpiration, 
atmospheric latent heat fluxes, and soil moisture, thereby influencing local and regional climate. 
Drought is often offset by management through groundwater withdrawals, with increasing 
pressure on these resources to maintain plant productivity. This results in indirect climate effects 
by altering land surface exchange of water and energy with the atmosphere (Marston et al. 2015). 

10.3.3 Forestry Considerations 

Climate change and land cover change in forested areas interact in many ways, such as through 
changes in mortality rates driven by changes in the frequency and magnitude of fire, insect 
infestations, and disease. In addition to the direct economic benefits of forestry, unquantified 
societal benefits include ecosystem services, like protection of watersheds and wildlife habitat, 
and recreation and human health value. United States forests and related wood products also 
absorb and store the equivalent of 16% of all CO 2 emitted by fossil fuel burning in the United 
States each year. Climate change is expected to reduce the carbon sink strength of forests overall. 

Effective management of forests offers the opportunity to reduce future climate change (for 
example, as given in proposals for Reduced Emissions from Deforestation and forest 
Degradation, or REDD+ (https://www.forestcarbonpartnership.org/what-redd), and in the Paris 
Agreement (see Ch. 14: Mitigation for more on the Paris Agreement) by capturing and storing 
carbon in forest ecosystems and long-term wood products (Lippke et al. 2011). Afforestation in 
the United States has the potential to capture and store 225 million tons of additional carbon per 
year from 2010 to 21 10 (EPA 2005; King et al. 2007). However, the projected maturation of 
United States forests (Wear and Coulston 2015) and land-cover change, in particular the 
expansion of urban and suburban areas along with projected increased demands for food and 
bioenergy, threaten the extent of forests and their carbon storage potential (McKinley et al. 

2011 ). 


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1 Large-scale die-off and disturbances resulting from climate change have potential effects beyond 

2 the biogeochemical and carbon cycle effects. Biogeophysical feedbacks can strengthen or reduce 

3 climate forcing. The low albedo of boreal forests provides a positive feedback, but those albedo 

4 effects are mitigated in tropical forests through evaporative cooling; for temperate forests, the 

5 evaporative effects are less clear (Bonan 2008). Changes in surface albedo, evaporation, and 

6 surface roughness can have feedbacks to local temperatures that are larger than the feedback due 

7 to the change in carbon sequestration (Jackson et al. 2008). Forest management frameworks 

8 (e.g., afforestation, deforestation, avoided deforestation) that account for biophysical (e.g., land 

9 surface albedo, surface roughness) properties can be used as climate protection or mitigation 

10 strategies (Anderson et al. 2011). 

1 1 Changes in growing season length, combined with drought and accompanying wildfire are 

12 reshaping California’s mountain ecosystems. The California drought led to the lowest snowpack 

13 in 500 years, the largest wildfires in post-settlement history, greater than 23% stress mortality in 

14 Sierra mid-elevation forests, and associated post-fire erosion. It is anticipated that slow recovery, 

15 possibly to different ecosystem types, with numerous shifts to species’ ranges will result in long- 

16 tenn changes to land surface biophysical as well as ecosystem structure and function in this 

17 region (Asner et al. 2016; http://www.fire.ca.gov/treetaskforce/). 

18 While changes in forest stocks, composition, and the ultimate use of forest products can 

19 influence net emissions and climate, the future net changes in forest stocks continue to be 

20 uncertain (US Department of State 2016). This uncertainty is due to a combination of 

21 uncertainties in future population size, population distribution and subsequent land-use change, 

22 harvest trends, wildfire management practices (for example, large-scale thinning of forests), and 

23 the impact of maturing U.S. forests. 

24 10.4 Urban Responses and Feedbacks to Climate Change: Urban Heat Island 

25 The urban heat island (UHI) effect is a well-known phenomenon in which urban environments 

26 often retain more heat than nearby rural environments, and it has a profound effect on the quality 

27 of life of the world’s growing urban population (Shepherd 2013). The UFII is characterized by 

28 increased surface and canopy temperatures as a result of heat-retaining asphalt and concrete, a 

29 lack of vegetation, and anthropogenic generation of heat and greenhouse gasses (Shepherd 

30 2013). Based on land surface temperature measurements, the UHI effect increases urban 

3 1 temperature by 2.9°C (5.2°F) on average, but it has been measured at 8°C (14.4°F) in cities built 

32 in areas dominated by temperate forests (Imhoff et al. 2010). In arid regions, however, urban 

33 areas can be greater than 2°C (3.6°F) cooler than surrounding shrublands (Bounoua et al. 2015). 

34 Similarly, urban settings lose up to 12% of precipitation through impervious surface runoff, 

35 versus just over 3% loss to runoff in vegetated regions. Carbon losses from the biosphere to the 

36 atmosphere through urbanization account for almost 2% of the continental total, a significant 

37 proportion given that urban areas only account for around 1% of land in the United States 

38 (Bounoua et al. 2015). 


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1 According to the World Bank, over 81% of the United States population currently resides in 

2 urban settings (http://data.worldbank.org/indicator/SP.URB. TOTL.IN.ZS?locations=US). 

3 Mitigation efforts are often stalled by the lack of quantitative data and understanding of the 

4 various factors that contribute to UHI. A recent study set out to quantitatively determine 

5 contributions to the intensity of UHI across North America (Zhao et al. 2014). The study found 

6 that population strongly influenced nighttime UHI, but that daytime UHI varied spatially 

7 following precipitation gradients. The model applied in this study indicated that the spatial 

8 variation in the UHI signal was controlled most strongly by impacts on the atmospheric 

9 convection efficiency. Because of the impracticality of managing convection efficiency, results 

10 from Zhao et al. (2014) support albedo management as an efficient strategy to mitigate UHI on a 

1 1 large scale. 



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Chapter 10 


1 TRACEABLE ACCOUNTS 

2 Key Finding 1 

3 Changes in land use and land cover due to human activities produce changes in surface albedo 

4 and in atmospheric aerosol and greenhouse gas concentrations. These combined effects have 

5 recently been estimated to account for 40% ± 16% of the human-caused global radiative forcing 

6 from 1850 to 2010 {high confidence). As a whole, the terrestrial biosphere (soil, plants) is a net 

7 “si nk ” for carbon (drawing down carbon from the atmosphere) and this sink has steadily 

8 increased since 1980, in part due to CO 2 fertilization {very high confidence). The future strength 

9 of the land sink is uncertain and dependent on ecosystem feedbacks; the possibility of the land 

10 becoming a net carbon source cannot be excluded {very high confidence). 

1 1 Description of evidence base 

12 Integrative modeling studies that combine climate models with models that simulate changes in 

13 land cover and land use have provided updated estimates climate forcing due to feedbacks 

14 among climate variables and land. Changes in land cover and land use are estimated to contribute 

15 40% of present climate forcing (0.9 W/m 2 ), and are estimated to contribute 0.9 to 1.9 W/nr in 

16 the year 2100 (Ward et al. 2015). This research is grounded in long-term observations that have 

17 been documented for over 40 years. For example, studies have documented physical land surface 

18 processes such as albedo, surface roughness, sensible and latent heat exchange, and land use and 

19 land cover change that interact with regional atmospheric processes (e.g., Marotz et al. 1975; 

20 Barnston and Schickendanz 1984; Alpert and Mandel 1986; Pielke and Zeng 1989; Pielke et 

21 2007). 

22 IPCC, 2013: Summary for Policymakers states: “From 1750 to 201 1, C02 emissions from fossil 

23 fuel combustion and cement production have released 375 [345 to 405] Gt C to the atmosphere, 

24 while deforestation and other land use change are estimated to have released 180 [100 to 260] 

25 GtC. This results in cumulative anthropogenic emissions of 555 [470 to 640] Gt C. {6.3 and 

26 WGI, Chapter 14 states for North America: “In summary, it is very likely that by mid-century the 

27 anthropogenic warming signal will be large compared to natural variability such as that 

28 stemming from the NAO, ENSO, PNA, PDO, and the NAMS in all North America regions 

29 throughout the year.” 

30 Major uncertainties 

3 1 Uncertainty exists in the future land cover and land use change. 

32 Assessment of confidence based on evidence and agreement, including short description of 

33 nature of evidence and level of agreement 

34 □ Certain (100%) 

35 X Very High 

36 □ High 

37 □ Medium 


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1 □ Low 

2 The existing impact on climate forcing has high confidence. The future forcing has lower 

3 confidence because it is difficult to estimate changes in land cover and land use into the future. 

4 However, if existing trends in land use and land cover change continue, the contribution of land 

5 cover to forcing will increase with high confidence. 

6 If appropriate, estimate likelihood of impact or consequence, including short description of 

7 basis of estimate 

8 □ Greater than 9 in 10 / Very Likely 

9 □ Greater than 2 in 3 / Likely 

10 □ About 1 in 2 / As Likely as Not 

11 □ Less than 1 in 3 / Unlikely 

12 □ Less than 1 in 10 / Very Unlikely 

13 Summary sentence or paragraph that integrates the above information 

14 The key finding is based on basic physics that has been well established for decades. Specific 

1 5 assessments, however, have not yet been made with regards to land cover and the climate 

16 system. 

17 

18 Key Finding 2 

19 The increased occurrence and severity of drought has led to large changes in plant community 

20 structure with subsequent effects on carbon distribution and cycling within ecosystems (for 

21 example, forests, grasslands). Uncertainties about future land use changes (for example, policy or 

22 mitigation measures) and about how climate change will affect land cover change make it 

23 difficult to project the magnitude and sign of future climate feedbacks from land cover changes. 

24 (High confidence) 

25 Description of evidence base 

26 From the perspective of the land biosphere, drought has strong effects on ecosystem productivity 

27 and carbon storage by reducing microbial activity and photosynthesis, and increasing the risk of 

28 wildfire, pest infestation, and disease susceptibility. Thus, droughts of the future will affect 

29 carbon uptake and storage, leading to feedbacks to the climate system (Schlesinger et al. 2016). 

30 Reduced productivity as a result of extreme drought events can also extend for several years 

3 1 post-drought (i.e., drought legacy effects; Frank et al. 2016; Reichstein et al. 2013; Anderegg et 

32 al. 2015). 

33 The most severe drought on record in Texas led to statewide regional tree mortality of 6.2%, or 

34 nearly 9X greater than the average annual mortality in this region of -0.7% (Moore et al. 2016). 

35 The net effect on carbon storage was estimated to be a redistribution of 24-30 Tg C from the live 

36 to dead tree carbon pool, which is equal to 6%-7% of pre-drought live tree carbon storage in 


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1 

2 

3 

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33 

34 

35 


Texas state forestlands or (Moore et al. 2016). This redistribution is that one singular event 
equals -36% of global carbon losses due to deforestation and land use change (Cias et al. 2013). 

Major uncertainties 

Major uncertainties include how future land use/land cover changes will occur as a result of 
policy and/or mitigation strategies in addition to climate change 



Assessment of confidence based on evidence and agreement, including short description of 
nature of evidence and level of agreement 

□ Certain (100%) 

□ Very High 
X High 

□ Medium 

□ Low 

If appropriate, estimate likelihood of impact or consequence, including short description of 
basis of estimate 

□ Greater than 9 in 10 / Very Likely 

□ Greater than 2 in 3 / Likely 

□ About 1 in 2 / As Likely as Not 

□ Less than 1 in 3 / Unlikely 

□ Less than 1 in 10 / Very Unlikely 

Summary sentence or paragraph that integrates the above information 

Future interactions between land cover and the climate system are uncertain and depend on both 
human decision making and the evolution of the climate system. 



Key Finding 3 

Since 1901, the consecutive number of both frost-free days and the length of the corresponding 
growing season has increased for all regions of the United States. However, there is important 
variability at smaller scales, with some locations showing decreases of as much as one to two 
weeks. Plant productivity has not increased linearly with the increased number of frost-free days 
or with the longer growing season due to temperature thresholds and requirements for growth as 
well as seasonal limitations in water and nutrient availability ( very high confidence). Future 
consequences of changes to the growing season for plant productivity are uncertain. 


Description of evidence base 

Without nutrient limitations, increased CO 2 concentrations and wann temperatures have been 
shown to extend the growing season, which may contribute to longer periods of plant activity 
and carbon uptake, but do not affect reproduction rates (Reyes-Fox et. al. 2014). However, other 


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34 

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confounding variables that coincide with climate change (for example, drought, increased ozone, 
and reduced photosynthesis due to increased or extreme heat) can offset increased growth 
associated with longer growing seasons (Adams et al. 2015). 

Major uncertainties 

Uncertainties exist in feedbacks among variables that impact the length of the growing season. 

Assessment of confidence based on evidence and agreement, including short description of 
nature of evidence and level of agreement 

□ Certain (100%) 

X Very High 

□ High 

□ Medium 

□ Low 

If appropriate, estimate likelihood of impact or consequence, including short description of 
basis of estimate 

□ Greater than 9 in 10 / Very Likely 

□ Greater than 2 in 3 / Likely 

□ About 1 in 2 / As Likely as Not 

□ Less than 1 in 3 / Unlikely 

□ Less than 1 in 10 / Very Unlikely 

Summary sentence or paragraph that integrates the above information 

Changes in growing season length and interactions with climate, biogeochemistry and land cover 
were covered in 12 chapters of NCA3, with no real summary statement. This key finding 
provides a summary of the complex nature of the growing season. 


Key Finding 4 

Surface temperatures are often higher in urban areas than in surrounding rural areas, for a 
number of reasons including the concentrated release of heat from buildings, vehicles, and 
industry. In the United States, this urban heat island (UHI) effect results in daytime temperatures 
0.9°-7.2°F (0.5°-4.0°C) higher and nighttime temperatures 1.8°- 4.5°F (1.0°-2.5°C) higher in 
urban areas, with larger temperature differences in humid regions (primarily the eastern United 
States) and in cities with larger populations. The UHI effect will strengthen in the future as the 
spatial extent and population of urban areas grow. ( High confidence) 

Description of evidence base 

The urban heat island (UHI) effect is correlated with the extent of impervious surfaces, which 
alter albedo or the saturation of radiation. The urban-rural difference that defines the UHI is 



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greatest for cities built in temperate forest ecosystems. The average temperature increase is 
2.9°C, except for urban areas in biomes with arid and semiarid climates (Imhoff et al. 2010) 

Major uncertainties 

No major uncertainties. 

Assessment of confidence based on evidence and agreement, including short description of 
nature of evidence and level of agreement 

□ Very High 
X High 

□ Medium 
n Low 

Land surface temperature estimates are taken from the MODIS-Aqua (MODI 1A2) satellite 
sensor with 99% confidence. 

If appropriate, estimate likelihood of impact or consequence, including short description of 
basis of estimate 

□ Greater than 9 in 10 / Very Likely 

□ Greater than 2 in 3 / Likely 

□ About 1 in 2 / As Likely as Not 

□ Less than 1 in 3 / Unlikely 

□ Less than 1 in 10 / Very Unlikely 

Summary sentence or paragraph that integrates the above information 

The key finding is based on satellite land surface measurements and analyzed by Imhoff et al. 
(2010). Bonoua et al. (2015) and Shepherd (2013) provide specific updates with regards to the 




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Chapter 10 


1 FIGURES 


2 

3 

4 



Some C stored 
in wood/paper 
products 


Natural Environment 


Anthropogenic Land Use and Land Cover Change 


Figure 10.1. A graphical representation of climate interactions with land use and land cover. 
(Figure source: Ward et ah, 2014) 



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1 

2 

3 

4 

5 

6 

7 

8 



Figure 10.2. Radiative forcings for land use/land cover change and other anthropogenic impacts 
estimated for the year 2010 referenced to the year 1850 (Ward et al. 2014). Total anthropogenic 
radiative forcings from (Myhre et al. 2013) are shown for comparison (yellow). Error lines 
represent uncertainties in total anthropogenic RF for the IPCC bars and uncertainties in land 
use/land cover change radiative forcings (Ward et al. 2014). The “SUM” bars show the total 
radiative forcings when all forcing agents are combined. (Figure source: Ward et al. 2014) 


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Observed Increase in Frost-Free Season Length 



Change in Annual Number of Days 



0-4 5-9 10-14 15 + 


2 Figure 10.3. Change in number of days since 1901 between the last spring occurrence of 32°F 

3 and first fall occurrence of 32°F for NCA4 regions of the United States. This change is 

4 expressed as the difference between the average number of frost-free days in 1986-2015 minus 

5 that 1901-1960. (Figure source: updated from Walsh et al. 2010). 

6 


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1 

2 

3 

4 

5 

6 

7 

8 


15 



-15 

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 

Year 

T>l 7 

Figure 10.4. The length of the growing season in the contiguous 48 states compared with a long- 
term average (1895-2015), where “growing season” is defined by a temperature threshold of 
41°F. For each year, the line represents the number of days shorter or longer than average. The 
line was smoothed using an 1 1 -year moving average. Choosing a different long-tenn average for 
comparison would not change the shape of the data over time. (Figure source: Kunkel et al. 

2016, EPA 2016). 



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12 Liu, J. Mao, Y. Pan, S. Peng, J. Penuelas, B. Poulter, T.A.M. Pugh, B.D. Stocker, N. Viovy, 

13 X. Wang, Y. Wang, Z. Xiao, H. Yang, S. Zaehle, and N. Zeng, 2016: Greening of the Earth 

14 and its drivers. Nature Climate Change, 6 , 791-795. http://dx.doi.org/10.1038/nclimate3004 



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1 11. Arctic Changes and their Effects on Alaska 

2 and the Rest of the United States 

3 KEY FINDINGS 

4 1 . For both the State of Alaska and for the Arctic as a whole, near-surface air temperature is 

5 increasing at a rate more than twice as fast as the global-average temperature. ( Very high 

6 confidence ) 

7 2. Rising Alaskan permafrost temperatures are causing permafrost to thaw and become more 

8 discontinuous; this releases additional CO 2 and CH4 resulting in additional warming (high 

9 confidence). The overall magnitude of the pennafrost-carbon feedback is uncertain. 

10 3. Arctic sea ice and Greenland Ice Sheet mass loss are accelerating and Alaskan mountain 

1 1 glaciers continue to melt (very high confidence). Alaskan coastal sea ice loss rates exceed the 

12 Arctic average (very high confidence). Observed sea and land ice loss across the Arctic is 

13 occurring faster than climate models predict ( very high confidence). Melting trends are 

14 expected to continue resulting in late summers becoming nearly ice-free for the Arctic ocean 

15 by mid-century ( very high confidence). 

16 4. Human activities have contributed to rising surface temperature, sea ice loss since 1979, and 

17 glacier mass loss observed across the Arctic. (High confidence) 

18 5. Atmospheric circulation patterns connect the climates of the Arctic and the United States. 

19 The mid-latitude circulation influences Arctic climate change (medium to high confidence) . 

20 In turn, current evidence suggests that Arctic wanning is influencing mid-latitude circulation 

21 over the continental United States and affecting weather patterns, but the mechanisms are not 

22 well understood (low to medium confidence). 

23 11.1. Introduction 

24 Climate changes in Alaska and across the Arctic continue to outpace changes occurring across 

25 the globe. The Arctic is a complex system integral to Earth’s climate, influencing global surface 

26 energy and moisture budgets, atmospheric and oceanic circulations, and geosphere-biosphere 

27 feedbacks. Resulting from its high sensitivity to radiative forcing and its role in amplifying 

28 wanning, the Arctic cryosphere is a key indicator of the global climate state. Accelerated melting 

29 of multiyear sea ice cover, mass loss from the Greenland Ice Sheet (GrIS), reduction of terrestrial 

30 snow cover, and permafrost degradation are stark examples of the rapid Arctic system-wide 

3 1 response to global warming. These changes in Arctic sea ice, land ice, surface temperature, and 

32 pennafrost influence global climate by affecting sea level, the carbon cycle, and potentially 

33 atmospheric and oceanic circulation patterns. Arctic climate change has altered the global 

34 climate in the past (Knies et al. 2014) and will influence climate in the future. Strongly coupled 


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1 to the Arctic, climate change in Alaska is apparent; the connection between climate changes in 

2 the Arctic and the continental United States is a topic of current research. 

3 Adaptation, mitigation, and policy decisions depend on projections of future Alaskan and Arctic 

4 climate. Aside from uncertainties due to natural variability, scientific uncertainty, and 

5 greenhouse gas emissions uncertainty (see Chapter 4), additional unique uncertainties in our 

6 understanding of Arctic processes thwart projections, including shortcomings in mixed-phase 

7 cloud processes (Wyser et al. 2008); boundary layer processes (Bourassa et al. 2013); sea ice 

8 mechanics (Bourassa et al. 2013); and ocean currents, eddies, and tides that affect the advection 

9 of heat into and around the Arctic Ocean (Maslowski et al. 2012, 2014). The inaccessibility of 

10 the Arctic has made it difficult to sustain the kind of high-quality observations of the atmosphere, 

1 1 ocean, land, and ice required to improve physically based models, stunting scientific progress. 

12 Improved data quality and increased observational coverage would help address important Arctic 

13 science questions. 

14 Despite these challenges, this chapter documents significant scientific progress and knowledge 

15 about how the Alaskan and Arctic climate has changed and will continue to change. 

16 11.2. Arctic Changes 

17 11.2.1. Alaska and Arctic Temperature 

1 8 Surface temperature — an essential component of the Arctic climate system — both drives and 

19 signifies change, fundamentally controlling the melting of sea ice, land ice, and snow. Further, 

20 the vertical profile of temperature modulates the exchange of mass, energy, and momentum 

21 between the surface and atmosphere, and influences other components such as clouds (Kay and 

22 Gettehnan 2009; Pavelsky et al. 2011; Taylor et al. 2015). Arctic temperatures exhibit significant 

23 spatial and interannual variability resulting from interactions and feedbacks between sea ice, 

24 snow cover, atmospheric heat transports, vegetation, clouds, water vapor, and the surface energy 

25 budget (Overland et al. 2015b; Johannessen et al. 2016; Overland and Wang 2016). 

26 Satellite observations show that the Arctic has warmed at rates more than twice as fast as the 

27 global average — by +0.60 ± 0.07°C (1.08° ± 0.1 3°F) per decade since 1981 — and that North 

28 American land regions north of 64°N (including Alaska) have warmed +0.54 ± 0.09°C (0.97° ± 

29 0. 16°F) per decade (Hartmann et al. 2013; Overland et al. 2014; Comiso and Hall 2014). Strong 

30 surface temperature wanning has occurred across Alaska, especially on the North Slope during 

3 1 autumn. For example, Barrow’s wanning since 1979 exceeds 3.8°C (7°F) in September, 6.6°C 

32 (12°F) in October, and 5.5°C (10°F) in November (Wendler et al. 2014). While Alaska state- 

33 wide annual mean temperature changes since 1949 are dominated by decadal variability like the 

34 Pacific Decadal Oscillation (Hartmann and Wendler 2005; McAfee 2014; see Ch. 5), records in 

35 2014 and 2015 broke previous marks by more than 0.5°C (1.0°F) (see Ch. 6). 


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1 The enhanced warming of Alaska and the Arctic is a robust feature of the climate response to 

2 anthropogenic forcing (Collins et al. 2013; Taylor et al. 2013). There is very likely an 

3 anthropogenic contribution to Alaskan surface temperature warming over the past 50 years 

4 (Bindoff et al. 2013; Gillett et al. 2008; Najafi et al. 2015). However, it is likely that other 

5 anthropogenic forcings (mostly aerosols) have partially offset the greenhouse gas warming since 

6 1913 by up to 60% at high latitudes and that natural forcing has not contributed to the long-term 

7 wanning in a discemable way (Najafi et al. 2015). According to this study, Arctic warming to 

8 date would have been larger without the offsetting aerosols influence. It is virtually certain that 

9 Arctic surface temperatures continue to increase faster than the global mean through the 2 1st 

10 century (Christensen et al. 2013). 

1 1 11.2.2. Arctic Sea Ice Change 

12 Arctic sea ice strongly influences Alaskan, Arctic, and global climate by modulating exchanges 

13 of mass, energy, and momentum between the ocean and the atmosphere. Variations in Arctic sea 

14 ice cover also influence atmospheric temperature and humidity, wind patterns, clouds, ocean 

15 temperature, thermal stratification, and ecosystem productivity (Kay and Gettehnan 2009; Kay et 

16 al. 2010; Pavelsky et al. 2011; Boisvert et al. 2013; Vaughan et al. 2013; Solomon et al. 2014; 

17 Taylor et al. 2015; Boisvert et al. 2015a, b; Johannessen et al. 2016). Arctic sea ice exhibits 

1 8 significant interannual, spatial, and seasonal variability driven by atmospheric wind patterns and 

19 cyclones, atmospheric temperature and humidity structure, clouds, radiation, sea ice dynamics, 

20 and the ocean (Ogi and Wallace 2007; Kwok and Untersteiner 2011; Stroeve et al. 2012a, b; Ogi 

21 and Rigor 2013; Carmack et al. 2015). Overwhelming evidence indicates that the character of 

22 Arctic sea ice is rapidly changing, marking the beginning of the “New Arctic” era. 

23 Observational evidence indicates Arctic-wide sea ice decline since 1979, accelerating melt since 

24 2000, and the fastest melt along the Alaskan coast (Stroeve et al. 20 14a, b; Comiso and Hall 

25 2014; Wendler et al. 2014). Although sea ice loss is found in all months, satellite observations 

26 show the fastest loss in late summer and autumn (Stroeve et al. 2014a). Since 1979, the annual 

27 average Arctic sea ice extent has decreased at a rate of 3.5%-4. 1% per decade, accelerating since 

28 2000 (Vaughan et al. 2013; Stroeve et al. 2014a, b; Comiso and Hall 2014). Regional sea ice melt 

29 along the Alaskan coasts exceeds the Arctic average rates with declines in the Beaufort and 

30 Chukchi Seas of -4.1% and -4.7% per decade, respectively. The annual minimum and 

3 1 maximum sea ice extent have decreased over the last 35 years by -13.3% and -2.5% per decade, 

32 respectively (Perovich et al. 2015). The ten lowest September sea ice extents over the satellite 

33 period have all occurred in the last ten years, the lowest in 2012. The 2016 September sea ice 

34 minimum tied with 2007 for the second lowest on record, but rapid refreezing resulted in the 

35 September monthly average extent being the fifth lowest. Despite the rapid initial refreezing, 

36 October and November sea ice extent is again in record low territory due to anomalously warm 

37 temperatures in the marginal seas around Alaska. 


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Other important characteristics of Arctic sea ice have also changed, including thickness, age, and 
melt season length. Sea ice thickness is monitored using an array of satellite, aircraft, and vessel 
measurements (Vaughan et al. 2013). The thickness of the Arctic sea ice during winter between 
1980 and 2008 has decreased between 1.3 and 2.3 meters (4.3 and 7.5 feet) (Vaughan et al. 

2013) . The age distribution of sea ice has become younger since 1988 where the extent of first- 
and multiyear sea ice has decreased in September by -1 1.5 ± 2.1% and -13.5 ± 2.5% per decade, 
respectively (Vaughan et al. 2013; Perovich et al. 2015). Figure 11.1 shows the September sea 
ice extent and age in 1984 and 2016, illustrating significant reductions in sea ice age (Tschudi et 
al. 2016). Younger, thinner sea ice is more susceptible to melt, therefore reductions in age and 
thickness imply a stronger interannual sea ice albedo feedback and larger interannual variability. 

[INSERT FIGURE 11.1 HERE: 

Figure 11.1: September sea ice extent (all gray-scale colors) and age shown for (a) 1984 and (b) 
2016, illustrating significant reductions in sea ice extent and age (thickness). Bar graph in the 
lower right of each panel illustrates the sea ice area covered within each age category. (Figure 
source: NASA Science Visualization Studio (http ://svs. gsfc.nasa.gov/cgi- 
bin/details.cgi?aid=4489); data: Tschudi et al. 2016) 

Sea ice melt season has lengthened Arctic-wide by at least five days per decade since 1979, with 
larger regional changes (Stroeve et al. 2014b; Parkinson 2014). Some of the largest observed 
changes in sea ice melt season (Figure 1 1.2) are found along Alaska’s northern and western 
coasts, lengthening the melt season by 20-30 days per decade and increasing the annual number 
of ice-free days by more than 90 (Parkinson 2014). Summer sea ice retreat along coastal Alaska 
has led to a longer open water seasons making the Alaskan coastline more vulnerable to erosion 
(Melillo 2014; Gibbs and Richmond 2015). 

[INSERT FIGURE 11.2 HERE: 

Figure 11.2: 35-year trends in Arctic sea ice melt season length in days per decade from passive 
microwave satellite observations, illustrating that the sea ice season has shortened by more than 
60 and as much as 90 days in coastal Alaska over the last 30 years. (Figure source: adapted from 
Parkinson 2014)] 

There is very likely an anthropogenic contribution to the observed changes in the Alaska and 
Arctic sea ice since 1979 (Bindoff et al. 2013). Internal climate variability alone could not have 
caused recently observed record low Arctic sea ice extents (Zhang and Knutson 2013). 

Additional sea ice loss across the Arctic is virtually certain to result in late summers very likely 
becoming nearly ice-free (areal extent less than 10 6 km 2 or approximately 3.9xl0 5 mi 2 ) by mid- 
century (Collins et al. 2013; Snape and Forster 2014). Natural variability (Wettstein and Deser 

2014) , future emissions, and model uncertainties (Gagne et al. 2015; Stroeve and Notz, 2015; 
Swart et al. 2015) all influence sea ice projections. A key message from the Third National 
Climate Assessment (NCA3; Melillo et al. 2014) was that Arctic sea ice is disappearing. The 


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1 fundamental conclusion of this assessment the same, additional research corroborates the NCA3 

2 statement. 

3 11,2.3. Arctic Ocean and Marginal Seas 

4 SEA SURFACE TEMPERATURE 

5 Arctic Ocean sea surface temperatures (SSTs) have increased since 1982. Satellite-observed 

6 Arctic Ocean SSTs, poleward of 60°N, exhibit a trend of +0.09 ± 0.01°C (+0.16 ± 0.02°F) per 

7 decade (Comiso and Hall 2014). Arctic Ocean SST is controlled by a combination of factors, 

8 including solar radiation and energy transport from ocean currents and atmospheric winds. 

9 Summertime Arctic Ocean SST trends and pattern strongly couple with sea ice extent; however, 

10 clouds, ocean color, upper-ocean thermal structure, and atmospheric circulation also play a role 

1 1 (Ogi and Rigor 2013; Rhein et al. 2013). Along coastal Alaska, SSTs in the Chukchi Sea exhibit 

12 a statistically significant (95% confidence) trend of 0.5 ± 0.3°C (+0.9 ± 0.5°F) per decade 

1 3 (Timmermans and Proshutinksy 2015). 

14 Arctic Ocean temperatures also increased at depth (Polyakov et al. 2012; Rhein et al. 2013). 

15 Since 1970, Arctic Ocean Intermediate Atlantic Water (AW) — located between 150 and 900 

16 meters — has warmed by 0.48 ± 0.05°C (0.86 ± 0.09°F) per decade; the most recent decade being 

17 the warmest (Polyakov et al. 2012). The observed AW wanning is unprecedented in the last 

18 1,150 years (Spielhagen et al. 2011; Jungclaus et al. 2014). The influence of AW wanning on 

19 future Alaska and Arctic sea ice loss is unclear (Doscher et al. 2014; Carmack et al. 2015). 

20 ALASKAN SEA LEVEL RISE 

21 The Alaskan coastline is vulnerable to sea level rise; however, strong regional variability exists 

22 in current trends and future projections. Sea level rise trends from the National Water Level 

23 Observation Network around Alaska reveal regional variations in the observed rate of sea level 

24 rise, with most stations experiencing slower rises than the global average. Several stations along 

25 Alaska’s southern coast have observed rises three times slower than the global values due to 

26 isostatic rebound and the proximity to Alaskan melting glaciers (Church et al. 2013; Ch. 12: Sea 

27 Level Rise). Tide gauge data show sea levels rising faster along the northern coast of Alaska but 

28 still slower than the global average. The largest future sea level rise in the Arctic is expected 

29 along the North Alaskan coast, exceeding a foot by 2100, but the magnitude depends 

30 significantly on the radiative forcing scenario and could reach 0.6 meters (approx. 2 feet) 

31 (Church et al. 2013). 

32 SALINITY 

33 Arctic Ocean salinity influences the freezing temperature of sea ice (less salty water freezes more 

34 readily) and the density profile representing the integrated effects of freshwater transport, river 

35 runoff, evaporation, and sea ice processes. Arctic Ocean salinity exhibits multidecadal 

36 variability, hampering the assessment of long-term trends (Rawlins et al. 2010). Emerging 


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1 evidence suggests that the Arctic Ocean and marginal sea salinity has decreased in recent years 

2 despite short-lived regional salinity increases between 2000 and 2005 (Rhein et al. 2013). 

3 Increased river runoff, rapid melting of sea and land ice, and changes in freshwater transport 

4 have influenced observed Arctic Ocean salinity (Rhein et al. 2013; Kohl and Serra 2014). 

5 OCEAN ACIDIFICATION 

6 Arctic Ocean acidification is occurring at a faster rate than the rest of the globe (Mathis et al. 

7 2015; Ch. 13: Ocean Acidification). Coastal Alaska and its ecosystems are especially vulnerable 

8 to ocean acidification because of the high sensitivity of Arctic Ocean water chemistry to changes 

9 in sea ice, respiration of organic matter, upwelling, and increasing river runoff (Mathis et al. 

10 2015). Sea ice loss and a longer melt season contribute to increased vulnerability of the Arctic 

1 1 Ocean to acidification by lowering total alkalinity, pennitting greater upwelling, and influencing 

12 the primary production characteristics in coastal Alaska (Arrigo et al. 2008; Cai et al. 2010; Hunt 

13 et al. 2011; Stabeno et al. 2012; Mathis et al. 2012; Bates et al. 2014). Global-scale modeling 

14 studies suggest that the largest and most rapid changes in pH are being observed and will 

15 continue to occur along Alaska’s coast, indicating that ocean acidification may increase enough 

16 by the 2030s to significantly influence coastal ecosystems (Mathis et al. 2015). 

17 11,2.4. Boreal Wildfires 

18 A global phenomenon with natural (lightning) and human-caused ignition sources, wildfire 

19 represents a critical ecosystem process that renews terrestrial habitats. Recent decades have seen 

20 increased forest fire activity in Alaska. Historically, however, wildfires have been less frequent 

21 and smaller in Alaska compared to the rest of the globe (Flannigan et al. 2009; Hu et al. 2015). 

22 Shortened land snow cover seasons and higher temperatures make the Arctic more vulnerable to 

23 wildfire (Flannigan et al. 2009; Hu et al. 2015; Young et al. 2016). Total area burned and the 

24 number of large fires (those with area greater than 1000 km 2 or 386 mi 2 ) in Alaska exhibits 

25 significant interannual and decadal scale variability, from influences of atmospheric circulation 

26 patterns and controlled burns, but have likely increased since 1959 (Kasischke and Turetsky 

27 2006). The most recent decade has seen an unusually large number of severe wildfire years in 

28 Alaska, for which the risk of severe fires has likely increased by 33%-50% as a result of 

29 anthropogenic climate change (Partain et al. 2016) and is projected to increase by up to a factor 

30 of four by the end of the century (Young et al. 2016). Alaska’s fire season is also likely 

3 1 lengthening — a trend expected to continue (Flannigan et al. 2009; Sanford et al. 2015). 

32 Thresholds in temperature and precipitation shape Arctic fire regimes, and projected increases in 

33 future lightning activity imply increased vulnerability to future climate change (Flannigan et al. 

34 2009; Young et al. 2016). Alaskan tundra and forest wildfires will likely increase under wanner 

35 and drier conditions (Sanford et al. 2015; French et al. 2015) and potentially result in a transition 

36 into a fire regime unprecedented in the last 10,000 years (Kelly et al. 2013). Total area burned is 

37 projected to increase between 25% and 53% by the end of the century (Joly et al. 2012). Existing 

38 studies do not demonstrate that the observed increased in forest fire activity over the historical 


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1 period has been highly unusual in comparison to natural variability. Rather, these studies have 

2 relied on model calculation to infer the human contribution. The degree of forestry management 

3 is a confounding factor which complicates attribution of changes to anthropogenic climate 

4 change. We conclude that there is medium confidence for a human-caused climate change 

5 contribution to increased forest fire activity in Alaska in recent decades. 

6 Boreal forests and tundra contain large stores of carbon, approximately 50% of the total global 

7 soil carbon (McGuire et al. 2009). Increased fire activity could deplete these stores, releasing 

8 them to the atmosphere to serve as an additional source of atmospheric CO 2 and alter the carbon 

9 cycle (McGuire et al. 2009; Kelly et al. 2016). Additionally, increased fires in Alaska may also 

10 enhance the degradation of Alaska’s permafrost, blackening the ground, reducing surface albedo, 

1 1 and removing protective vegetation. 

12 11.2.5. Snow Cover and Permafrost 

13 Snow cover, like sea ice, possesses a high albedo and serves as a climate feedback. Snow cover 

14 extent has significantly decreased across the Northern Hemisphere and Alaska over the last 

15 decade (Derksen and Brown 2012; see also Ch. 7: Precipitation Change and Ch. 10: Land 

16 Cover). Northern Hemisphere June snow cover decreased by more than 50% between 1967 and 

17 2012 (Brown and Robinson 2011; Vaughan et al. 2013), at trend of-19.8% per decade (Derksen 

18 et al. 2015). May snow cover has also declined, at -7.3% per decade, due to reduced winter 

19 accumulation from warmer temperatures. Regional trends in snow cover duration vary, with 

20 some showing earlier onsets while others show later onsets (Derksen et al. 2015). In Alaska, the 

21 2016 May statewide snow coverage 595,000 km 2 (-372,000 mi 2 ) was the lowest on record dating 

22 back to 1967; the snow coverage of 2015 was the second lowest and 2014 was the fourth lowest. 

23 Declining snow cover is expected to continue; however, the evolution of Arctic ecosystems, 

24 including the observed tundra shrub expansion (Myers-Smith et al. 2011), can alter the snow 

25 depth, melt dynamics, and the local surface energy budget influencing melt. 

26 Alaska and Arctic permafrost characteristics have responded to increased temperatures and 

27 reduced snow cover in most regions since the 1980s (AMAP 2011; Vaughan et al. 2013). The 

28 permafrost wanning rate varies regionally; however, colder permafrost is warming faster than 

29 wanner pennafrost (Vaughan et al. 2013; Romanovsky et al. 2015). This feature is most evident 

30 across Alaska, where permafrost on the North Slope is warming more rapidly than in the interior. 

3 1 Permafrost temperatures across the North Slope at various depths ranging from 12 to 20 meters 

32 (39 to 65 feet) have wanned between 0.2° and 0.7°C (0.3° and 1.3°F) per decade since 2000 

33 (Figure 1 1.3; Romanovsky et al. 2016). Trends in the permafrost active layer show strong 

34 regional variations (AMAP 2011; Shiklomanov et al. 2012); however, active layer thickness 

35 increased across much of the Arctic (Vaughan et al. 2013). Uncertainties in future permafrost 

36 wanning and active layer deepening in Alaska are due to poorly understood deep soil, ice wedge, 

37 and thermokarst processes that may accelerate the thaw (Koven et al. 2015a; Liljedahl et al. 


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2016). Continued degradation of permafrost and a transition from continuous to discontinuous 
pennafrost is expected over the 21st century (Vaughan et al. 2013). 

[INSERT FIGURE 11.3 HERE: 

Figure 11.3: Time series of annual mean pennafrost temperatures (units: °F) at various depths 

from 12 to 20 meters (39 to 65 feet) from 1977 through 2015 at several sites across Alaska, 

including the North Slope continuous permafrost region, and the discontinuous permafrost in 

Alaska and northwestern Canada. Solid lines represent the linear trends drawn to highlight that 

permafrost temperatures are warming faster in the colder, coastal permafrost regions than the 
warmer interior regions. (Figure Source: adapted from Romanovsky et al. 2016; © American 
Meteorological Society, used with permission)] 

11.2.6. Continental Ice Sheets and Mountain Glaciers 

Mass loss from ice sheets and glaciers influences sea level rise (see Ch. 12: Sea Level Rise), the 
oceanic thermohaline circulation, and the global energy budget. Changes in GrIS can also 
influence Alaskan climate by altering Arctic-wide and midlatitude circulation patterns (Section 
1 1.3.1). Observational and modeling studies indicate that GrIS and glaciers in Alaska are out of 
balance with current climate conditions and losing mass (Vaughan et al. 2013; Zemp et al. 2015). 
In recent years, mass loss has accelerated and is expect to continue (Zemp et al. 2015; Harig and 
Simons 2016). 

Dramatic changes have occurred across GrIS, particularly at its margins. GrIS average annual 
mass loss from January 2003 to May 2013 was -244 ± 6 Gt per year (Harig and Simons 2016). 
Increased surface melt, runoff, and increased outlet glacier discharge from warmer surface air 
temperatures are the primary factors contributing to mass loss (Howat et al. 2008; van den 
Broeke et al. 2009; Rignot et al. 2010; Straneo et al. 2011; Khan et al. 2014). The effects of 
wanner air and ocean temperatures on the GrIS mass balance can be amplified by ice dynamical 
feedbacks, such as faster sliding, greater calving, and increased submarine melting (Joughin et al. 
2008; Holland et al. 2008a; Rignot et al. 2010; Bartholomew et al. 2011). Shallow ocean 
warming and regional changes in the ocean and atmospheric circulation are increasing mass loss 
(Dupont and Alley 2005; Lim et al. 2016; Tedesco et al. 2016). The underlying mechanisms of 
the recent speed-up of discharge remain unclear (Straneo et al. 2010; Johannessen et al. 2011); 
however, warming subsurface ocean temperatures, atmospheric warming (Velicogna 2009; van 
den Broeke et al. 2009, Andresen et al. 2012), and meltwater penetration to the glacier bed 
(Johannessen et al. 2011, Mernild et al. 2012) very likely contribute. 

Annual average ice mass from Arctic-wide glaciers has decreased every year since 1984 (AMAP 
2011; Pelto 2015; Zemp et al. 2015), with significant losses in Alaska, especially over the past 
two decades (Figure 1 1.4; Vaughan et al. 2013; Sharp et al. 2015; Harig and Simons 2016). 
Glacial mass loss around the Gulf of Alaska region has declined steadily since 2003 (Harig and 
Simons 2016). NASA’s Gravity Recovery and Climate Experiment (GRACE) indicates mass 


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